Antievolution.org :: Antievolution.org Discussion BoardThe Critic's Resource on Antievolution

 Antievolution.org Discussion Board > From the Panda's Thumb > After the Bar Closes... > Evolutionary Computation

 Pages: (12) < [1] 2 3 4 5 6 ... >
 Topic: Evolutionary Computation, Stuff that drives AEs nuts < Next Oldest | Next Newest >
Wesley R. Elsberry

Posts: 4117
Joined: May 2002

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

"weasel" math

Given:

Base set size K (number of possible characters at each position)

Target string length L

Mutation rate (per site) u

Population size N

here are some basic probabilities to go with a "weasel" run.

Per base:

Pincorrect to correct = 1 / K

Pcorrect to incorrect = (K - 1) / K

Blind search:

Ptry is all correct = K-L

Pa try in the population is all correct = N * K-L

Expected number of correct bases when all bases are changed = L  / K

Expected number of correct bases when a genome is produced via copy with mutation = u * L  / K

In "weasel" run:

Expected number of correct bases given a partially matching string:
Given C as number of correct matching bases

expected correct bases after mutation = C + (u * (L - C) / K) - (u * C * (K - 1) / K)

There's a few more items to derive to pull in the population parameter, but I need to go now.

Edit: Equations for Ptry is all correct and dependencies per PT comment by Mike Elzinga.

Edited by Wesley R. Elsberry on Mar. 19 2009,19:40

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

More "weasel" math

Probability that a candidate will retain all the correct letters from its parent: (1 - (u * (k - 1) / k))C

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Richardthughes

Posts: 9475
Joined: Jan. 2006

Can you derive an optimal mutation rate?

--------------
"Richardthughes, you magnificent bastard, I stand in awe of you..." : Arden Chatfield
"You magnificent bastard! " : Louis
"ATBC poster child", "I have to agree with Rich.." : DaveTard
"...it was Richardthughes making me lie in bed.." : Kristine

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

 Quote (Richardthughes @ Mar. 18 2009,11:24) Can you derive an optimal mutation rate?

I'll have to think about that some. Later.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

More "weasel" math:

Probability of a candidate changing a parent's correct base to an incorrect base = PCandidate_C2I =

(1 - (1 - (u * (K - 1) / K))C)

Probability that a population will have at least one candidate that preserves all the correct bases from the parent of the previous generation = PPopulation_C2C =

1 - (PCandidate_C2I )N

Checked via Monte Carlo methods, and using the N=50 and u=0.05 values that (IIRC) ROb was often using:

 Code Sample 1000 runs, 00 correct : p_c2c calc = 1.00000, MC = 1.00000; p_c2i calc = 0.00000, MC = 0.000001000 runs, N=50, u=0.05000, K=27, C=0, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.000001000 runs, 01 correct : p_c2c calc = 0.95185, MC = 0.93800; p_c2i calc = 0.04815, MC = 0.062001000 runs, N=50, u=0.05000, K=27, C=1, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.047261000 runs, 02 correct : p_c2c calc = 0.90602, MC = 0.90400; p_c2i calc = 0.09398, MC = 0.096001000 runs, N=50, u=0.05000, K=27, C=2, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.094761000 runs, 03 correct : p_c2c calc = 0.86240, MC = 0.85900; p_c2i calc = 0.13760, MC = 0.141001000 runs, N=50, u=0.05000, K=27, C=3, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.135921000 runs, 04 correct : p_c2c calc = 0.82088, MC = 0.82400; p_c2i calc = 0.17912, MC = 0.176001000 runs, N=50, u=0.05000, K=27, C=4, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.178101000 runs, 05 correct : p_c2c calc = 0.78135, MC = 0.80400; p_c2i calc = 0.21865, MC = 0.196001000 runs, N=50, u=0.05000, K=27, C=5, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.216181000 runs, 06 correct : p_c2c calc = 0.74373, MC = 0.75900; p_c2i calc = 0.25627, MC = 0.241001000 runs, N=50, u=0.05000, K=27, C=6, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.258481000 runs, 07 correct : p_c2c calc = 0.70792, MC = 0.74400; p_c2i calc = 0.29208, MC = 0.256001000 runs, N=50, u=0.05000, K=27, C=7, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.292261000 runs, 08 correct : p_c2c calc = 0.67384, MC = 0.67200; p_c2i calc = 0.32616, MC = 0.328001000 runs, N=50, u=0.05000, K=27, C=8, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.324601000 runs, 09 correct : p_c2c calc = 0.64139, MC = 0.62100; p_c2i calc = 0.35861, MC = 0.379001000 runs, N=50, u=0.05000, K=27, C=9, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.360861000 runs, 10 correct : p_c2c calc = 0.61051, MC = 0.61000; p_c2i calc = 0.38949, MC = 0.390001000 runs, N=50, u=0.05000, K=27, C=10, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.389661000 runs, 11 correct : p_c2c calc = 0.58112, MC = 0.59500; p_c2i calc = 0.41888, MC = 0.405001000 runs, N=50, u=0.05000, K=27, C=11, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.420601000 runs, 12 correct : p_c2c calc = 0.55314, MC = 0.54600; p_c2i calc = 0.44686, MC = 0.454001000 runs, N=50, u=0.05000, K=27, C=12, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.447941000 runs, 13 correct : p_c2c calc = 0.52650, MC = 0.52000; p_c2i calc = 0.47350, MC = 0.480001000 runs, N=50, u=0.05000, K=27, C=13, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.470501000 runs, 14 correct : p_c2c calc = 0.50115, MC = 0.50900; p_c2i calc = 0.49885, MC = 0.491001000 runs, N=50, u=0.05000, K=27, C=14, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.501301000 runs, 15 correct : p_c2c calc = 0.47702, MC = 0.45800; p_c2i calc = 0.52298, MC = 0.542001000 runs, N=50, u=0.05000, K=27, C=15, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.516581000 runs, 16 correct : p_c2c calc = 0.45406, MC = 0.48200; p_c2i calc = 0.54594, MC = 0.518001000 runs, N=50, u=0.05000, K=27, C=16, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.542701000 runs, 17 correct : p_c2c calc = 0.43219, MC = 0.41800; p_c2i calc = 0.56781, MC = 0.582001000 runs, N=50, u=0.05000, K=27, C=17, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.567081000 runs, 18 correct : p_c2c calc = 0.41139, MC = 0.41200; p_c2i calc = 0.58861, MC = 0.588001000 runs, N=50, u=0.05000, K=27, C=18, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.592181000 runs, 19 correct : p_c2c calc = 0.39158, MC = 0.35000; p_c2i calc = 0.60842, MC = 0.650001000 runs, N=50, u=0.05000, K=27, C=19, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.610701000 runs, 20 correct : p_c2c calc = 0.37272, MC = 0.37200; p_c2i calc = 0.62728, MC = 0.628001000 runs, N=50, u=0.05000, K=27, C=20, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.627621000 runs, 21 correct : p_c2c calc = 0.35478, MC = 0.33300; p_c2i calc = 0.64522, MC = 0.667001000 runs, N=50, u=0.05000, K=27, C=21, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.640481000 runs, 22 correct : p_c2c calc = 0.33770, MC = 0.32200; p_c2i calc = 0.66230, MC = 0.678001000 runs, N=50, u=0.05000, K=27, C=22, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.661461000 runs, 23 correct : p_c2c calc = 0.32144, MC = 0.31500; p_c2i calc = 0.67856, MC = 0.685001000 runs, N=50, u=0.05000, K=27, C=23, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.678541000 runs, 24 correct : p_c2c calc = 0.30596, MC = 0.28900; p_c2i calc = 0.69404, MC = 0.711001000 runs, N=50, u=0.05000, K=27, C=24, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.693801000 runs, 25 correct : p_c2c calc = 0.29123, MC = 0.28000; p_c2i calc = 0.70877, MC = 0.720001000 runs, N=50, u=0.05000, K=27, C=25, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.707921000 runs, 26 correct : p_c2c calc = 0.27721, MC = 0.27700; p_c2i calc = 0.72279, MC = 0.723001000 runs, N=50, u=0.05000, K=27, C=26, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.721541000 runs, 27 correct : p_c2c calc = 0.26386, MC = 0.23500; p_c2i calc = 0.73614, MC = 0.765001000 runs, N=50, u=0.05000, K=27, C=27, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.736441000 runs, 28 correct : p_c2c calc = 0.25116, MC = 0.24500; p_c2i calc = 0.74884, MC = 0.755001000 runs, N=50, u=0.05000, K=27, C=28, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.74932

The above completely explains why a list showing the best candidate from each generation is highly unlikely to show any change in a correct character when a candidate bearing it was selected as best in a previous generation. The proportion of candidates that had a change of a correct character to an incorrect one nonetheless rises to almost three-quarters of each generation when almost all characters are correct.

Now doing the Monte Carlo methods on the situation with N=12 and u=0.18, where I picked N and u in order to get a range of values for the population that went down to a relatively small probability.

 Code Sample 1000 runs, 00 correct : p_c2c calc = 1.00000, MC = 1.00000; p_c2i calc = 0.00000, MC = 0.000001000 runs, N=12, u=0.18000, K=27, C=0, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.000001000 runs, 01 correct : p_c2c calc = 0.82667, MC = 0.82700; p_c2i calc = 0.17333, MC = 0.173001000 runs, N=12, u=0.18000, K=27, C=1, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.170581000 runs, 02 correct : p_c2c calc = 0.68338, MC = 0.69300; p_c2i calc = 0.31662, MC = 0.307001000 runs, N=12, u=0.18000, K=27, C=2, p_pop_c2c calc = 1.00000, MC = 1.00000Proportion of candidates w/C2I bases = 0.322331000 runs, 03 correct : p_c2c calc = 0.56493, MC = 0.56600; p_c2i calc = 0.43507, MC = 0.434001000 runs, N=12, u=0.18000, K=27, C=3, p_pop_c2c calc = 0.99995, MC = 1.00000Proportion of candidates w/C2I bases = 0.441171000 runs, 04 correct : p_c2c calc = 0.46701, MC = 0.48600; p_c2i calc = 0.53299, MC = 0.514001000 runs, N=12, u=0.18000, K=27, C=4, p_pop_c2c calc = 0.99947, MC = 1.00000Proportion of candidates w/C2I bases = 0.535081000 runs, 05 correct : p_c2c calc = 0.38606, MC = 0.39800; p_c2i calc = 0.61394, MC = 0.602001000 runs, N=12, u=0.18000, K=27, C=5, p_pop_c2c calc = 0.99713, MC = 0.99700Proportion of candidates w/C2I bases = 0.611671000 runs, 06 correct : p_c2c calc = 0.31914, MC = 0.32100; p_c2i calc = 0.68086, MC = 0.679001000 runs, N=12, u=0.18000, K=27, C=6, p_pop_c2c calc = 0.99008, MC = 0.99200Proportion of candidates w/C2I bases = 0.679671000 runs, 07 correct : p_c2c calc = 0.26382, MC = 0.25100; p_c2i calc = 0.73618, MC = 0.749001000 runs, N=12, u=0.18000, K=27, C=7, p_pop_c2c calc = 0.97466, MC = 0.97200Proportion of candidates w/C2I bases = 0.734751000 runs, 08 correct : p_c2c calc = 0.21809, MC = 0.23600; p_c2i calc = 0.78191, MC = 0.764001000 runs, N=12, u=0.18000, K=27, C=8, p_pop_c2c calc = 0.94778, MC = 0.95500Proportion of candidates w/C2I bases = 0.783831000 runs, 09 correct : p_c2c calc = 0.18029, MC = 0.19100; p_c2i calc = 0.81971, MC = 0.809001000 runs, N=12, u=0.18000, K=27, C=9, p_pop_c2c calc = 0.90797, MC = 0.91400Proportion of candidates w/C2I bases = 0.814921000 runs, 10 correct : p_c2c calc = 0.14904, MC = 0.16000; p_c2i calc = 0.85096, MC = 0.840001000 runs, N=12, u=0.18000, K=27, C=10, p_pop_c2c calc = 0.85582, MC = 0.85600Proportion of candidates w/C2I bases = 0.856671000 runs, 11 correct : p_c2c calc = 0.12321, MC = 0.12900; p_c2i calc = 0.87679, MC = 0.871001000 runs, N=12, u=0.18000, K=27, C=11, p_pop_c2c calc = 0.79357, MC = 0.78600Proportion of candidates w/C2I bases = 0.880831000 runs, 12 correct : p_c2c calc = 0.10185, MC = 0.09800; p_c2i calc = 0.89815, MC = 0.902001000 runs, N=12, u=0.18000, K=27, C=12, p_pop_c2c calc = 0.72446, MC = 0.72100Proportion of candidates w/C2I bases = 0.899001000 runs, 13 correct : p_c2c calc = 0.08420, MC = 0.08100; p_c2i calc = 0.91580, MC = 0.919001000 runs, N=12, u=0.18000, K=27, C=13, p_pop_c2c calc = 0.65196, MC = 0.66100Proportion of candidates w/C2I bases = 0.914581000 runs, 14 correct : p_c2c calc = 0.06960, MC = 0.07600; p_c2i calc = 0.93040, MC = 0.924001000 runs, N=12, u=0.18000, K=27, C=14, p_pop_c2c calc = 0.57925, MC = 0.55400Proportion of candidates w/C2I bases = 0.931921000 runs, 15 correct : p_c2c calc = 0.05754, MC = 0.06100; p_c2i calc = 0.94246, MC = 0.939001000 runs, N=12, u=0.18000, K=27, C=15, p_pop_c2c calc = 0.50891, MC = 0.50000Proportion of candidates w/C2I bases = 0.943331000 runs, 16 correct : p_c2c calc = 0.04756, MC = 0.03900; p_c2i calc = 0.95244, MC = 0.961001000 runs, N=12, u=0.18000, K=27, C=16, p_pop_c2c calc = 0.44278, MC = 0.45400Proportion of candidates w/C2I bases = 0.950831000 runs, 17 correct : p_c2c calc = 0.03932, MC = 0.03800; p_c2i calc = 0.96068, MC = 0.962001000 runs, N=12, u=0.18000, K=27, C=17, p_pop_c2c calc = 0.38206, MC = 0.38200Proportion of candidates w/C2I bases = 0.961831000 runs, 18 correct : p_c2c calc = 0.03250, MC = 0.03600; p_c2i calc = 0.96750, MC = 0.964001000 runs, N=12, u=0.18000, K=27, C=18, p_pop_c2c calc = 0.32736, MC = 0.32200Proportion of candidates w/C2I bases = 0.968751000 runs, 19 correct : p_c2c calc = 0.02687, MC = 0.03200; p_c2i calc = 0.97313, MC = 0.968001000 runs, N=12, u=0.18000, K=27, C=19, p_pop_c2c calc = 0.27881, MC = 0.25300Proportion of candidates w/C2I bases = 0.976001000 runs, 20 correct : p_c2c calc = 0.02221, MC = 0.02000; p_c2i calc = 0.97779, MC = 0.980001000 runs, N=12, u=0.18000, K=27, C=20, p_pop_c2c calc = 0.23629, MC = 0.23400Proportion of candidates w/C2I bases = 0.978171000 runs, 21 correct : p_c2c calc = 0.01836, MC = 0.01800; p_c2i calc = 0.98164, MC = 0.982001000 runs, N=12, u=0.18000, K=27, C=21, p_pop_c2c calc = 0.19941, MC = 0.19100Proportion of candidates w/C2I bases = 0.983081000 runs, 22 correct : p_c2c calc = 0.01518, MC = 0.01600; p_c2i calc = 0.98482, MC = 0.984001000 runs, N=12, u=0.18000, K=27, C=22, p_pop_c2c calc = 0.16769, MC = 0.17300Proportion of candidates w/C2I bases = 0.984581000 runs, 23 correct : p_c2c calc = 0.01255, MC = 0.00600; p_c2i calc = 0.98745, MC = 0.994001000 runs, N=12, u=0.18000, K=27, C=23, p_pop_c2c calc = 0.14061, MC = 0.15400Proportion of candidates w/C2I bases = 0.986171000 runs, 24 correct : p_c2c calc = 0.01037, MC = 0.01400; p_c2i calc = 0.98963, MC = 0.986001000 runs, N=12, u=0.18000, K=27, C=24, p_pop_c2c calc = 0.11762, MC = 0.11200Proportion of candidates w/C2I bases = 0.990421000 runs, 25 correct : p_c2c calc = 0.00858, MC = 0.00600; p_c2i calc = 0.99142, MC = 0.994001000 runs, N=12, u=0.18000, K=27, C=25, p_pop_c2c calc = 0.09819, MC = 0.10400Proportion of candidates w/C2I bases = 0.990751000 runs, 26 correct : p_c2c calc = 0.00709, MC = 0.01200; p_c2i calc = 0.99291, MC = 0.988001000 runs, N=12, u=0.18000, K=27, C=26, p_pop_c2c calc = 0.08183, MC = 0.08000Proportion of candidates w/C2I bases = 0.992831000 runs, 27 correct : p_c2c calc = 0.00586, MC = 0.00700; p_c2i calc = 0.99414, MC = 0.993001000 runs, N=12, u=0.18000, K=27, C=27, p_pop_c2c calc = 0.06810, MC = 0.07400Proportion of candidates w/C2I bases = 0.993581000 runs, 28 correct : p_c2c calc = 0.00484, MC = 0.00600; p_c2i calc = 0.99516, MC = 0.994001000 runs, N=12, u=0.18000, K=27, C=28, p_pop_c2c calc = 0.05661, MC = 0.04400Proportion of candidates w/C2I bases = 0.99617

The above shows that in order to have low probabilities that the best candidate in a generation will retain all the characters that were correct in the parent, one must have small N and relatively high u values.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Here's something for David...

Population size is on the X axis, running from 1 to 500. Mutation rate is on the Y axis, running from 0.0 (bottom of image) to 1.0. The lighter the pixel, the better the chance of convergence. This was generated by finding the PPopulation_C2C(K-1) for each condition represented by the pixel and scaling that probability over 1,024 grayscale values.

As expected, there is no local sensitivity to change in parameters.

Expanding the population scale by ten gives this:

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

dvunkannon

Posts: 1359
Joined: June 2008

Some Id-ists have trouble understanding why abstractions like GA/EC are relevant - ie. but it ain't wet! An important point for these folks (and others) is that GA isn't a model of evolution, it _IS_ evolution.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Huh... I just realized that I should have done the graphs up for (L-1) instead of (K-1). It's the difference between 26 and 27, so it won't make a big shift, but I'll generate those later when I get a chance.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

AmandaHuginKiss

Posts: 150
Joined: Dec. 2008

 Quote (dvunkannon @ Mar. 19 2009,07:57) Some Id-ists have trouble understanding why abstractions like GA/EC are relevant - ie. but it ain't wet! An important point for these folks (and others) is that GA isn't a model of evolution, it _IS_ evolution.

That's one thing that I would like to try if I had time is to model the wet evolution.

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (Wesley R. Elsberry @ Mar. 18 2009,15:17) Here's something for David...Population size is on the X axis, running from 1 to 500. Mutation rate is on the Y axis, running from 0.0 (bottom of image) to 1.0. The lighter the pixel, the better the chance of convergence. This was generated by finding the PPopulation_C2C(K-1) for each condition represented by the pixel and scaling that probability over 1,024 grayscale values.As expected, there is no local sensitivity to change in parameters. Expanding the population scale by ten gives this:

Thank you Wes!

Sometimes people are stunned by complexity, but these images are so simple that most people don't see the significance. Evolution just works.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

"weasel" graph of PPopulation_C2C(L-1):

ETA: Again, population from 1 to 500 is on the X axis, and mutation probability from 0 to 1.0 is on the Y axis.

Comparison of (K-1) v. (L-1) versions of the graph (lighter is less different):

Edited by Wesley R. Elsberry on Mar. 19 2009,12:57

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Freelurker

Posts: 76
Joined: Oct. 2006

 Quote (dvunkannon @ Mar. 18 2009,15:57) Some Id-ists have trouble understanding why abstractions like GA/EC are relevant - ie. but it ain't wet! An important point for these folks (and others) is that GA isn't a model of evolution, it _IS_ evolution.

This is true in one sense, but let's not lose the distinction between genetic optimization algorithms and simulations of biological evolution.

It seems to me that Dembski makes mischief in just this way. All this criticism of modelers "sneaking in" information just isn't relevant to simulation models. The entire model, every bit of it, came from the modeler. The real issue is the fidelity of the model; does it match reality sufficiently to justify any conclusions one makes based on the model.

--------------
Invoking intelligent design in science is like invoking gremlins in engineering. [after Mark Isaak.]
All models are wrong, some models are useful. - George E. P. Box

dvunkannon

Posts: 1359
Joined: June 2008

Quote (Freelurker @ Mar. 19 2009,12:35)
 Quote (dvunkannon @ Mar. 18 2009,15:57) Some Id-ists have trouble understanding why abstractions like GA/EC are relevant - ie. but it ain't wet! An important point for these folks (and others) is that GA isn't a model of evolution, it _IS_ evolution.

This is true in one sense, but let's not lose the distinction between genetic optimization algorithms and simulations of biological evolution.

It seems to me that Dembski makes mischief in just this way. All this criticism of modelers "sneaking in" information just isn't relevant to simulation models. The entire model, every bit of it, came from the modeler. The real issue is the fidelity of the model; does it match reality sufficiently to justify any conclusions one makes based on the model.

I agree. There are folks who deny evolution can exist at all, and there are those who deny what biology does is evolution.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Richardthughes

Posts: 9475
Joined: Jan. 2006

EIL's math page for 'Weasel':

http://www.evoinfo.org/WeaselMath.html

--------------
"Richardthughes, you magnificent bastard, I stand in awe of you..." : Arden Chatfield
"You magnificent bastard! " : Louis
"ATBC poster child", "I have to agree with Rich.." : DaveTard
"...it was Richardthughes making me lie in bed.." : Kristine

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Did you notice that there wasn't any math there for the "weasel" as described by Dawkins? Just Dembski/Marks "partitioned search" and "deterministic search".

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Richardthughes

Posts: 9475
Joined: Jan. 2006

 Quote (Wesley R. Elsberry @ Mar. 19 2009,18:39) Did you notice that there wasn't any math there for the "weasel" as described by Dawkins? Just Dembski/Marks "partitioned search" and "deterministic search".

Yes. They're very keen to frame it as it isn't. I'm convinced Dembski still doesn't 'get' GAs.

--------------
"Richardthughes, you magnificent bastard, I stand in awe of you..." : Arden Chatfield
"You magnificent bastard! " : Louis
"ATBC poster child", "I have to agree with Rich.." : DaveTard
"...it was Richardthughes making me lie in bed.." : Kristine

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Here's an interesting graph:

I've put a 20 pixel border around this. On the X axis, there is the number of correct letters (treated as a continuous scale), and mutation rate is on the Y axis. I've taken terms from the "expected number of correct letters in a mutated string" calculation and subtracted the term for expected conversion of correct to incorrect from the expected conversion of incorrect to correct. Black is a net 28 expected new incorrect letters, white is a net 2 expected new correct letters, and the border color is where the two terms cancel each other out. One can see at a glance that as one considers candidates with more matching letters, only lower mutation rates are going to give a good chance of matching all the letters.

And here's the same graph, but with the net 1 expected new incorrect values shifted to black, too, making a contour visible, and showing how the mutation rate interacts with expectations for new candidate strings:

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

"weasel" versus "partitioned search"

I derived an equation for expectation of correct bases following mutation in "weasel" (see above for earlier reference):

 Quote expected correct bases after mutation in "weasel" = C + (u * (L - C) / K) - (u * C * (K - 1) / K)

"Partitioned search" would be the case where:

 Quote expected correct bases after mutation in PS = C + (u * (L - C) / K) - (0 * u * C * (K - 1) / K)= C + (u * (L - C) / K) - 0= C + (u * (L - C) / K)

"Locking" or "latching" is the same as removing the term that allows for correct bases to mutate to incorrect ones. What remains is an expectation that the number of correct bases can only monotonically increase.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Jkrebs

Posts: 327
Joined: Sep. 2004

Hi.

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (Richardthughes @ Mar. 18 2009,12:24) Can you derive an optimal mutation rate?

Start with an optimal population size. Goldberg's research suggests  N= 1.4L, where L is the length of the problem description (and therefore the population members) in bits. That is a good bit higher than the commonplace 50.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

I get N=178 for that. Is that what you get?

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (Wesley R. Elsberry @ Mar. 25 2009,19:59) I get N=178 for that. Is that what you get?

Yeah, I guessed 27 log 2 was around 4.5 so I got 177.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

dvunkannon

Posts: 1359
Joined: June 2008

Quote (dvunkannon @ Mar. 25 2009,21:09)
 Quote (Wesley R. Elsberry @ Mar. 25 2009,19:59) I get N=178 for that. Is that what you get?

Yeah, I guessed 27 log 2 was around 4.5 so I got 177.

I should mention that most of Goldberg's research is in GAs using only a selection operator and a recombination operator, no mutation. This despite publishing papers (see the "Ready to Rumble" series) that show mutation is the more efficient operator in some broad classes of problems.

Since Weasel is really a (1,n)-ES, not a selectorecombinative GA, that population sizing heuristic might not be completely appropriate. But I don't know of other work with as firm a footing.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

While statistically unlikely, successive recombination operations can produce the same changes as point mutation can (this is dependent on having a population with good diversity, of course), so it isn't surprising that recombination might be used as a sole mechanism for change.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Avida applied to evolutionary biology

The Beneficial Effect of Deleterious Mutations

If they put something up on the work experimenting to test Sewall Wright's shifting-balance theory, I'll post the link.

Edited by Wesley R. Elsberry on Mar. 27 2009,20:29

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Henry J

Posts: 3735
Joined: Mar. 2005

But wouldn't recombination by itself continually reduce the amount of diversity in the gene pool, and eventually producing a deficit of it?

Henry

Richardthughes

Posts: 9475
Joined: Jan. 2006

Pharyngula on "Weasel":

http://scienceblogs.com/pharyng....put.php

--------------
"Richardthughes, you magnificent bastard, I stand in awe of you..." : Arden Chatfield
"You magnificent bastard! " : Louis
"ATBC poster child", "I have to agree with Rich.." : DaveTard
"...it was Richardthughes making me lie in bed.." : Kristine

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

 Quote (Henry J @ Mar. 27 2009,13:28) But wouldn't recombination by itself continually reduce the amount of diversity in the gene pool, and eventually producing a deficit of it?Henry

Both genetic drift and natural selection reduce variation, but I wouldn't think it is primarily the choice of mutation modality that affects that.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (Henry J @ Mar. 27 2009,14:28) But wouldn't recombination by itself continually reduce the amount of diversity in the gene pool, and eventually producing a deficit of it?Henry

Yes, recombination and selection lead to convergence, hopefully on the correct allele. Goldberg's Design of innovation is a great resource on these issues in GAs.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

dvunkannon

Posts: 1359
Joined: June 2008

In the past, I've worked with the ECJ package from Sean Luke's group at George Mason University. It has support for (mu, lamda)-ES built in. I might have time to build a weasel in ECJ. I think it might be a matter of setting up the parameter file right, all the code is already there.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

dvunkannon

Posts: 1359
Joined: June 2008

Not specifically Weasel...

I just read this paper on sexual selection in GA. I was thinking of trying to reproduce (ahem) some of the results. It seems the researchers made a bunch of changes to the standard GA, and I'd like to see which were responsible for the positive variations they report.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

The antievolutionist software, Mendel's Accountant, asserts that it "allows realistic numerical simulation of the mutation/selection process over time".

Discussion at Theology Web, though, indicates that the program may not deliver results in accordance with known population genetics.

Gary Hurd suggested that this would be a good topic for a TOA FAQ. We can use this thread to help coordinate people working on an analysis of Mendel's Accountant.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

AnsgarSeraph

Posts: 11
Joined: June 2009

Hi, all —

I'm another migrant from TWeb; I've got Mendel's Accountant set up on a 32-bit and a 64-bit system. I certainly won't be much help with any actual knowledge but I'm very willing to run simulations for anyone who doesn't have/doesn't want Mendel set up on their computer.

I've found that Mendel will allow my 4GB setup to run small populations (~1000) for about 40,000 generations or larger populations (~10,000) for less than 10,000 generations. I plan on purchasing some extra RAM soon so I might be able to extend the runs a bit. I did not see much difference in latitude running a 64-bit setup but extra RAM might change that.

The user manual for Mendel's Accountant is here. If helpful, I can (hopefully) attach screen grabs of the advanced Mendel settings so people don't need to hunt through the manual.

—Sam

AnsgarSeraph

Posts: 11
Joined: June 2009

Sorry. The manual is HERE:

Mendel's Accountant User Manual

There is also a Linux how-to but the SourceForge page does not have the tarball listed. Some advanced features of MENDEL require Linux. They are almost certainly unnecessary for a FAQ but I'll e-mail the maintainer of the code (Dr. Brewer, I think) and try to get that.

—Sam

Dr.GH

Posts: 1864
Joined: May 2002

Howdy Sam. Glad to see you here. I'll assume that you have seen the prior discussion on this site starting about about here.

I hope to be away all day tomorrow, so I hope that everyone will start right to work while I go fishing.  :D

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

Richardthughes

Posts: 9475
Joined: Jan. 2006

Also:

--------------
"Richardthughes, you magnificent bastard, I stand in awe of you..." : Arden Chatfield
"You magnificent bastard! " : Louis
"ATBC poster child", "I have to agree with Rich.." : DaveTard
"...it was Richardthughes making me lie in bed.." : Kristine

utidjian

Posts: 185
Joined: Oct. 2007

Ok... I downloaded the .exe files. Both the earlier version and the update from sourceforge.

I am running Linux (Fedora 10) on a Intel iMac with 1G of RAM.

I unpacked the files with Wine. First the older version and then the newer one. Man it installs a lot of stuff.

The Linux source is in
/home/utidjian/.wine/drive_c/Mendel/Source
on my system. Not much in there.

listing:
 Code Sample [utidjian@istrain Source]\$ ls -oghtotal 416K-rw-rw-rw- 1 4.5K 2008-09-13 17:38 common.h-rw-rw-rw- 1  587 2008-09-07 22:00 Interface back-end.lnk-rw-rw-rw- 1  661 2008-09-07 22:00 Interface front-end.lnk-rw-rw-rw- 1  985 2008-09-01 20:51 Makefile-rw-rw-rw- 1 165K 2008-10-01 06:15 mendel.f-rw-rw-rw- 1 163K 2008-09-05 22:00 mendel.f.bak-rw-rw-rw- 1 1.9K 2008-09-18 18:57 mendel.in-rw-rw-rw- 1 1.5K 2008-09-04 03:20 mpi_mendel.f-rw-rw-rw- 1  42K 2006-03-01 13:02 random_pkg.f90-rw-rw-rw- 1 1.3K 2007-01-15 09:50 sort.f90

The main file in there is mendel.f. Lots of comments. I can "read" Fortran but I don't know diddly about Population Genetics.

Time for bed.

-DU-

--------------
Being laughed at doesn't mean you're progressing along some line. It probably just means you're saying some stupid shit -stevestory

Zachriel

Posts: 2560
Joined: Sep. 2006

I upgraded to Mendel 1.4.1. It acts differently with the parameters I tried in the original version. I started with the defaults changing only the fraction of beneficial mutations and maximum effect of beneficial mutations parameters.

1.0000000 frac_fav_mutn
1.0000000 max_fav_fitness_gain

Now the fitness increases in a linear fashion. So they must have fixed a major bug in between versions. I'll continue to test over the next few days.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Dr.GH

Posts: 1864
Joined: May 2002

I collected the various "articles" writen about MA;

Larry Vardiman
2008. “The "Fatal Flaws" of Darwinian Theory” Acts & Facts. 37 (7): 6. Institute of Creation Research
http://www.icr.org/article/fatal-flaws-darwinian-theory/

Money quote:

 Quote “Mendel's Accountant provides overwhelming empirical evidence that all of the "fatal flaws" inherent in evolutionary genetic theory are real. This leaves evolutionary genetic theory effectively falsified--with a degree of certainty that should satisfy any reasonable and open-minded person.”

John Sanford, John Baumgardner, Wesley Brewer, Paul Gibson, Walter ReMine
2008a “Using Numerical Simulation to Test the Validity of Neo-Darwinian Theory” In A. A. Snelling (Ed.) (2008). Proceedings of the Sixth International Conference on Creationism (pp. 165–175). Pittsburgh, PA: Creation Science Fellowship and Dallas, TX: Institute for Creation Research.

Baumgardner, J., Sanford, J., Brewer, W., Gibson, P., & ReMine, W.
2008b “Mendel’s Accountant: A new population genetics simulation tool for studying mutation and natural selection.” In A. A. Snelling (Ed.), Proceedings of the sixth international conference on creationism (pp. 87–98). Pittsburgh, Pennsylvania: Creation Science Fellowship & Dallas, Texas: Institute for Creation Research.

Sanford, J., Baumgardner, J., Gibson, P., Brewer, W., & ReMine, W.
(2007a). Mendel’s Accountant: A biologically realistic forward-time population genetics program. Scalable Computing: Practice and Experience 8(2), 147–165. http://www.scpe.org.

Sanford, J., Baumgardner, J., Gibson, P., Brewer, W., & ReMine, W.
(2007b). Using computer simulation to understand mutation accumulation dynamics and genetic load. In Y. Shi, G. D. van Albada, J. Dongarra, & P. M. A. Sloot (Eds.), International Conference on Computer Sscience 2007, Part II, Lecture Notes in Computational Science 4488 (pp. 386–392). Springer-Verlag: Berlin, Heidelberg.

Edited by Dr.GH on June 11 2009,09:33

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

So, how much "evolution sucks" verbiage was generated by the people running the pre-1.4.1 versions? Was there any notice that people should re-run their experiments due to a pretty drastic change in program behavior?

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Dr.GH

Posts: 1864
Joined: May 2002

 Quote (Wesley R. Elsberry @ June 11 2009,09:29) So, how much "evolution sucks" verbiage was generated by the people running the pre-1.4.1 versions? Was there any notice that people should re-run their experiments due to a pretty drastic change in program behavior?

Exactly!

PS: The links all work now, and I am going fishing.

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

AnsgarSeraph

Posts: 11
Joined: June 2009

 Quote (Wesley R. Elsberry @ June 11 2009,11:29) So, how much "evolution sucks" verbiage was generated by the people running the pre-1.4.1 versions? Was there any notice that people should re-run their experiments due to a pretty drastic change in program behavior?

To be fair (to a shoddy program? How odd), v. 1.4.1 still "demonstrates" all the genetic entropy problems that Sanford makes noise about. Whatever they fixed in terms of beneficial mutations, our runs at TWeb using 1.4.1 indicate that it's nowhere near enough; a 90% beneficial mutation rate with default "maximal benefit effect" still reduces fitness in a linear manner.

—Sam

AnsgarSeraph

Posts: 11
Joined: June 2009

 Quote (Wesley R. Elsberry @ June 11 2009,11:29) So, how much "evolution sucks" verbiage was generated by the people running the pre-1.4.1 versions? Was there any notice that people should re-run their experiments due to a pretty drastic change in program behavior?

To be fair (to a shoddy program? How odd), v. 1.4.1 still "demonstrates" all the genetic entropy problems that Sanford makes noise about. Whatever they fixed in terms of beneficial mutations, our runs at TWeb using 1.4.1 indicate that it's nowhere near enough; a 90% beneficial mutation rate with default "maximal benefit effect" still reduces fitness in a linear manner.

—Sam

utidjian

Posts: 185
Joined: Oct. 2007

Is anyone else playing with the source (or even reading it)?
I also found this file in the Source folder:

 Code Sample [utidjian@buttle Source]\$ cat mendel.in        1000    pop_size         500    num_generations           1    fitness_distrib_type:exponential_mutation_effect           2    selection_scheme:unrestricted_probability_selection          23    haploid_chromosome_number        1000    num_linkage_subunits    0.000000    pop_growth_rate           0    pop_growth_model:fixed_population   3.000e+08    haploid_genome_size   6.0000000    offspring_per_female   0.0000000    fraction_random_death   0.0000000    fraction_self_fertilization  10.0000000    new_mutn_per_offspring   0.0010000    high_impact_mutn_fraction   0.1000000    high_impact_mutn_threshold   0.0010000    uniform_fitness_effect_del   0.0000000    multiplicative_weighting   1.000e-05    tracking_threshold   0.0000000    fraction_recessive   0.0000000    recessive_hetero_expression   0.5000000    dominant_hetero_expression   0.0000000    frac_fav_mutn   0.0010000    max_fav_fitness_gain   0.2000000    heritability   0.0000000    non_scaling_noise   0.5000000    partial_truncation_value           0    num_contrasting_alleles   0.0000000    initial_alleles_mean_effect   0.9000000    linked_mutn_se_fraction   1.0000000    se_scaling_factor           0    synergistic_epistasis           0    clonal_reproduction           0    clonal_haploid           1    dynamic_linkage           0    fitness_dependent_fertility           0    is_parallel           0    bottleneck_yes        1000    bottleneck_generation         100    bottleneck_pop_size         500    num_bottleneck_generations           0    num_initial_fav_mutn           1    num_indiv_exchanged           1    migration_generations           1    migration_model           1 homogenous_tribes       47469 max_tracked_mutn_per_indiv          42 random_number_seed           0 write_dump           0 restart_case           1 restart_dump_numbertest01 case_id/.           2 num_tribes           2 num_procs           0 plot_avg_data           0 restart_case_id           1 restart_appendbatch run_queue           0 c_engine

Anything interesting in there?

-DU-

--------------
Being laughed at doesn't mean you're progressing along some line. It probably just means you're saying some stupid shit -stevestory

Posts: 3138
Joined: Mar. 2008

 Quote (Richardthughes @ Mar. 27 2009,17:08) Pharyngula on "Weasel":http://scienceblogs.com/pharyng....put.php

 Quote As Ian Musgrave shows, the program is trivial, and even us biologists can whip one out in minutes.

Even non-biologists...

http://www.itatsi.com

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

If anybody is trying to install MA, but has a more recent Perl and unchecks the Perl install, be warned that the CGI has a fixed location that it expects to launch the Perl executable from, C:\Mendel\Perl\bin\perl.exe

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

AnsgarSeraph

Posts: 11
Joined: June 2009

Quote (utidjian @ June 11 2009,12:17)
Is anyone else playing with the source (or even reading it)?
I also found this file in the Source folder:

 Code Sample 47469 max_tracked_mutn_per_indiv

Anything interesting in there?

-DU-

That number is considerably higher than the input parameters on my runs; ranging from a population of 1000 to a population of 10,000, I think the highest setting on my runs for that was ~25,000 tracked mutations per individual. I'm not sure how they calculate for that parameter. This might be one of the limiting factors in testing MENDEL, as I have already run up against a "Favorable mutation count exceeds limit" error.

—Sam

Steve Schaffner

Posts: 13
Joined: June 2009

I may have missed something, but I haven't seen anything in the v. 1.4.1 runs that was clearly wrong, i.e. that suggested a bug in the implementation (other than the broken option for fixed selection coefficient). Which doesn't mean there aren't any bugs, but they are not obvious.

What is clear is that the default parameters for beneficial alleles are very low. Their justification for having such a low maximum beneficial effect strikes me as plausible-sounding nonsense.

It's also clear that their basic model is not one from evolutionary biology. The essential process they're modeling is the accumulation of mildly deleterious mutations, ones that have such a small functional effect that they are invisible to natural selection. This only occurs because the population starts out in a state of genetic perfection, compared to which the new mutations are deleterious. A real population would never have become that optimized, precisely because the different choices of allele are indistinguishable by NS.

For those who have the program running . . . Can it provide more output? Comparing results with theory would be much more straightforward if one could count only the number of mutations that have fixed, rather than all present in the population; it would also be useful to see the allele frequency spectrum. (And if it can't do those things, then it is of no interest as a population genetics tool.)

AnsgarSeraph

Posts: 11
Joined: June 2009

 Quote (Steve Schaffner @ June 11 2009,13:02) For those who have the program running . . . Can it provide more output? Comparing results with theory would be much more straightforward if one could count only the number of mutations that have fixed, rather than all present in the population; it would also be useful to see the allele frequency spectrum. (And if it can't do those things, then it is of no interest as a population genetics tool.)

Is the allele frequency spectrum you're looking for in here?

Box.net - human1 sample Folder

I think what you're looking for might be titled "human1_plm.png".

The output files in MENDEL do give a count for the number of fixed alleles; it's near the bottom of the output file and looks like this:

 Code Sample Allele summary statistics (tracked mutations only):    (Statistics are based on       891517 tracked deleterious mutations                         and            0 tracked   favorable mutations.)     Very rare   Polymorphic     Fixed      Total       (0-1%)      (1-99%)      (100%)        15107        8202           0       23309 deleterious            0           0           0           0 favorable

—Sam

Henry J

Posts: 3735
Joined: Mar. 2005

I used to be able to do that, but college was decades ago and the language probably "evolved" since then.

 Quote Anything interesting in there?-DU-

Yes:

 Quote 10.0000000    new_mutn_per_offspring

I'm no biologist, but that sounds high to me.

Henry

Posts: 3138
Joined: Mar. 2008

 Quote What is clear is that the default parameters for beneficial alleles are very low. Their justification for having such a low maximum beneficial effect strikes me as plausible-sounding nonsense.

From my own dabbling I think the only really critical factor in getting a GA to "work" is an effective fitness function. Assuming at least some offspring are viable, the fitness function must be able to see and score alleles, either by seeing them directly of by having some means of scoring phenotypes.

All the other aspects are pretty much irrelevant.

My own effort tries to evolve a population of letter strings that "look like" words, without having a fixed target. The trick is having a fitness function that can score relative wordness without requiring enormous computational resourses.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

I just ran Zachriel's modified parameter set under v1.2.1 and v.1.4.1. Both used exactly the same "mendel.in" configuration file. Only one each, but the v1.4.1 run does go to completion and shows the accumulation and fixation of favorable mutations. The v1.2.1 run, by contrast, shows a declining population that only lasts to generation 31. The value for "before sel: geno fitness" looks particularly strange; in the final generation, the value was -90.5. In the v1.4.1 run, that value was never less than 1.0, and at generation 500 had reached a little over 20. I'm assuming at the moment that the "fitness" value is always with respect to the original absolute value at the start of the run.

Whatever else may be going on, it does seem that MA treatment of favorable mutations changed rather radically between those versions. I wouldn't want to validate v1.4.1 on this basis, but it comparatively is doing a much better job than v1.2.1, and I think my earlier comment stands: outcomes of experiments performed with MA v1.2.1 (and perhaps earlier versions) should be treated with skepticism until independently confirmed, preferably with a package that can be validated against actual popgen results.

ETA: Using Mendel's Accountant v.1.2.1 is like using a bank that inexplicably only records your withdrawals and fails to record your deposits.

Edited by Wesley R. Elsberry on June 11 2009,13:57

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

oldmanintheskydidntdoit

Posts: 4999
Joined: July 2006

 Quote (midwifetoad @ June 11 2009,13:40) The trick is having a fitness function that can score relative wordness without requiring enormous computational resourses.

I wonder if google could help, after all it "suggests" words when it cannot match your search term. No suggestion = not "wordy" enough.

OK it's probably not practical, not for running quickly anyway.

Still....

--------------
I also mentioned that He'd have to give me a thorough explanation as to *why* I must "eat human babies".
FTK

if there are even critical flaws in Gauger’s work, the evo mat narrative cannot stand
Gordon Mullings

Posts: 3138
Joined: Mar. 2008

Quote (oldmanintheskydidntdoit @ June 11 2009,13:47)
 Quote (midwifetoad @ June 11 2009,13:40) The trick is having a fitness function that can score relative wordness without requiring enormous computational resourses.

I wonder if google could help, after all it "suggests" words when it cannot match your search term. No suggestion = not "wordy" enough.

OK it's probably not practical, not for running quickly anyway.

Still....

Google runs pretty quickly for me. I'm a bit in awe of their ability to suggest words from misspellings, but after my experience evolving words I think I know how they do it. Or at least one approach that doesn't require a supercomputer.

I think my approach is more effective than that used by most spelling checkers.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

sledgehammer

Posts: 529
Joined: Sep. 2008

I haven't run the program, but perusing the description of the algorithm, it seem to me that this section that describes how fitness is assigned is the part that determines the ultimate behavior of the model.  They clearly have built in an asymmetry in the fitness of beneficial vs deleterious mutations, and their justifications of the asymmetry smell fishy to me, but IANAB. (bolding mine)

 Quote To provide users of Mendel even more flexibility in specifying the fitness effect distribution, we have chosen to use aform of the Weibull function [12] that is a generalization of the more usual exponential function. Our function, expressedby eq. (3.1), maps a random number x, drawn from a set of uniformly distributed random numbers, to a fitness effect d(x)for a given random mutation.d(x) = (dsf) exp(?ax^gamma), 0 < x < 1. (3.1)Here (dsf) is the scale factor which is equal to the extreme value which d(x) assumes when x = 0. We allow this scalefactor to have two separate values, one for deleterious mutations and the other for favorable ones. These scale factors are meaningful relative to the initial fitness value assumed for the population before we introduce new mutations. In Mendel we assume this initial fitness value to be 1.0. For deleterious mutations, since lethal mutations exist, we choose dsf del = ?1. For favorable mutations, we allow the user to specify the (positive) scale factor dsf fav. Normally, this would be a small value (e.g., 0.01 to 0.1), since it is only in very special situations that a single beneficial mutation wouldhave a very large effect.The parameters a and gamma, both positive real numbers, determine the shape of the fitness effect distribution. We applythe same values of a and gamma to both favorable and deleterious mutations. The parameter a determines the minimum absolute values for d(x), realized when x = 1. We choose to make the minimum absolute value of d(x) the inverse of the haploid genome size G (measured in number of nucleotides) by choosing a = loge(G). For example, for the human genome, G = 3 × 109, which means that for the case of deleterious mutations, d(1) = ?1/G = ?3 × 10?10. For large genomes,this minimum value is essentially 0. For organisms with smaller genomes such as yeast, which has a value for G onthe order of 107, the minimum absolute effect is larger. This is consistent with the expectation that each nucleotide in a smaller genome on average plays a greater relative role in the organism’s fitness.The second parameter gamma, can be viewed as ontrolling the fraction of mutations that have a large absolute fitnesseffect. Instead of specifying gamma directly, we select two quantities that are more intuitive and together define gamma. The first is theta, a threshold value that defines a “high-impact mutation”. The second is q, the fraction of mutations that exceed this threshold in their effect. For example, a user can first define a high-impact mutation as one that results in 10% or more change in fitness (theta = 0.1) relative to the scale factor and then specify that 0.001 of all mutations (q = 0.001) be in this category. Inside the code the value of is computed that satisfies these requirements. We reiterate that Mendel uses the same value for gamma, and thus the same values for theta and q, for both favorable and deleterious mutations. Figure 3.1 shows the effect of the parameter q on the shape of the distribution of fitness effect. Note that for each of the cases displayed the large majority of mutations are nearly neutral, that is, they have very small effects. Since a utation’s effect on fitness can be measured experimentally only if it is sufficiently large, our strategy for parameterizing the fitness effect distribution in terms of high-impact situtations provides a means for the Mendel user to relate the numerical model input more directly to available data regarding the actual measurable frequencies of mutations in a given biological context.

Part of the justification for asymmetry is that some mutations are lethal, meaning that individual has zero probability of reproducing.  OK, but the maximum fitness benefit of a beneficial mutation is "a very small number like 0.001", which is then subject to "heritability factor", typically 0.2, and other probabilities that severely limit its ability to propagate.
To make matters worse, for some unjustified reason, the same distribution for beneficial and deleterious is used, after severely skewing the results with the above.
Again, IANOB, but it seems to me that a single beneficial mutation can, in many situations like disease resistance, blonde hair, big boobs, etc, virtually guarantee mating success, just like a deleterious mutation can be reproductively lethal.
I can see easily how the skewed treatment of beneficial vs deleterious mutations could virtually guarantee "genetic entropy", as evidenced by monotonically decreasing population fitness caused by accumulation of deleterious mutational load.

Sanford, J., Baumgardner, J., Gibson, P., Brewer, W., & ReMine, W.
(2007a). Mendel’s Accountant: A biologically realistic forward-time population genetics program. Scalable Computing: Practice and Experience 8(2), 147–165.

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (AnsgarSeraph @ June 11 2009,11:41)

 Quote (Wesley R. Elsberry @ June 11 2009,11:29) So, how much "evolution sucks" verbiage was generated by the people running the pre-1.4.1 versions? Was there any notice that people should re-run their experiments due to a pretty drastic change in program behavior?

To be fair (to a shoddy program? How odd), v. 1.4.1 still "demonstrates" all the genetic entropy problems that Sanford makes noise about. Whatever they fixed in terms of beneficial mutations, our runs at TWeb using 1.4.1 indicate that it's nowhere near enough; a 90% beneficial mutation rate with default "maximal benefit effect" still reduces fitness in a linear manner.

—Sam

I think a key to understanding Mendel's Accountant is the so-called "Maximal beneficial mutation effects". It defaults to an extremely low number.

 Quote Mendel's Accountant User Manual: Maximal beneficial mutation effects – A realistic upper limit must be placed upon beneficial mutations. This is because a single nucleotide change can expand total biological functionality of an organism only to a limited degree. The larger the genome and the greater the total genomic information, the less a single nucleotide is likely to increase the total. Researchers must make a judgment for themselves of what is a reasonable maximal value for a single base change. The MENDEL default value for this limit is 0.001. This limit implies that a single point mutation can increase total biological functionality by as much as 0.1%. In a genome such as man’s, assuming only 10% of the genome is functional, such a maximal impact point mutation might be viewed as equivalent to adding 300,000 new information-bearing base pairs each of which had the genome-wide average fitness contribution. Researchers need to honestly define the upper limit they feel is realistic for their species. However it should be obvious that, in all cases, the upper limit for beneficial mutation effects ought to correspond to a very small fraction of the total genomic information (i.e. a small number relative to one).

There is something wrong with the analysis. They're comparing the selective value of a change to adding thousands of new bases to the genome. But adding 10% to a genome doesn't necessarily make an organism 10% fitter. On the other hand, a small change can often have a very high selective value. Consider a mutation making someone resistant to plague. Maybe he just tastes bad to fleas.

Also, I'm not sure what the number is supposed to represent. Does a value of 1 mean a change in fitness of 1? Shouldn't this scale with absolute fitness? Or is it a fractional? So does 1 represent 100% or a doubling of fitness? PS. I'm guessing the former, but my Accounting time has been somewhat limited.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

sledgehammer

Posts: 529
Joined: Sep. 2008

Call me skeptical, but I think  that they put the various "hooks" to skew the beneficial vs deleterious effects into the program for one reason only.

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Has anyone seen anything in MA that deals with compensatory mutations?

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Posts: 3091
Joined: May 2006

For those that need a fair (and free!) Population Genetics text, here's one available in PDF form, in a RAR-compressed file, for personal use:

Alan R. Templeton, “Population Genetics and Microevolutionary Theory”
Wiley-Liss; 1st edition (September 29, 2006) 705 pages

http://depositfiles.com/en/files/2390240 (8.05 MB)

Sanford's "genomic (mutational) meltdown" scenarios are a hoot. Even DaveScot was bright enough to see that Sanford's proposed mutation rates were out of line with reality: fast-reproducing sexual species that have existed a few million should have all been extinct by now, but they're not. Sanford inflates deleterious mutation rates and disregards compensatory mechanisms.

--------------
AtBC Award for Thoroughness in the Face of Creationism

mammuthus

Posts: 13
Joined: June 2009

 Quote (deadman_932 @ June 11 2009,15:57) Sanford's "genomic (mutational) meltdown" scenarios are a hoot. Even DaveScot was bright enough to see that Sanford's proposed mutation rates were out of line with reality: fast-reproducing sexual species that have existed a few million should have all been extinct by now, but they're not. Sanford inflates deleterious mutation rates and disregards compensatory mechanisms.

His argument is a little more involved than that.  It seems to revolve around genome size; the smaller genome size of something like P.falciparum prevents genetic meltdown, but it would occur with larger genome sized mammals.  So genetic entropy is a problem for the latter (if not on Sanford YEC timescales).  You can't just take fast reproducing things like P.falciparum and apply the Genetic Entropy failure in this case widely.  At least that's how I read it.

 Quote It occured to me recently that Sanford’s projected rate of genetic decay doesn’t square with the observed performance of P.falciparum. P.falciparum’s genome is about 23 million nucleotides. At Sanford’s lowest given rate of nucleotide copy errors that means each individual P.falciparum should have, on average, about 3 nucleotide errors compared to its immediate parent. If those are nearly neutral but slightly deleterious mutations (as the vast majority of eukaryote mutations appear to be) then the number should be quite sufficient to cause a genetic meltdown from their accumulation over the course of billions of trillions of replications. Near neutral mutations are invisible to natural selection but the accumulation of same will eventually become selectable. If all individuals accumulate errors the result is decreasing fitness and natural selection will eventually kill every last individual (extinction). Yet P.falciparum clearly didn’t melt down but rather demonstrated an amazing ability to keep its genome perfectly intact. How?After thinking about it for a while I believe I found the answer - the widely given rate of eukaryote replication errors is correct. If P.falciparum individuals get an average DNA copy error rate of one in one billion nucleotides then it follows that approximately 97% of all replications result in a perfect copy of the parent genome. That’s accurate enough to keep a genome that size intact. An enviromental catastrophe such as an ice age which lowers temperatures even at the equator below the minimum of ~60F in which P.falciparum can survive would cause it to become extinct while genetic meltdown will not. Mammals however, with an average genome size 100 times that of P.falciparum, would have an average of 3 replication errors in each individual. Thus mammalian genomes would indeed be subject to genetic decay over a large number of generations which handily explains why the average length of time between emergence to extinction for mammals and other multicelled organisms with similar genome sizes is about 10 million years if the fossil and geological evidence paints an accurate picture of the past. I DO believe the fossil and geological records present us with an incontrovertible picture of progressive phenotype evolution that occured over a period of billions of years. I don’t disbelieve common ancestry and phenotype evolution by descent with modification - I question the assertion that random mutation is the ultimate source of modification which drove phylogenetic diversification.

Posts: 3138
Joined: Mar. 2008

 Quote fast-reproducing sexual species that have existed a few million should have all been extinct by now, but they're not.

Isn't this the ultimate test of a simulation -- that it must model the fact that populations don't go extinct simply because their genes degrade?

Any simulation where this happens is obviously flawed. History trumps trumps any theory that says something that has happened can't happen.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

OK, why are there still Amoeba dubia around? I haven't found an explicit statement of average generation time for the species, but it is likely on the order of 24 hours based on generation times for other amoebae. Its genome is about 670 billion base pairs. That would seem to qualify as a large genome, wouldn't it?

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

mammuthus

Posts: 13
Joined: June 2009

 Quote (Wesley R. Elsberry @ June 11 2009,18:30) OK, why are there still Amoeba dubia around? I haven't found an explicit statement of average generation time for the species, but it is likely on the order of 24 hours based on generation times for other amoebae. Its genome is about 670 billion base pairs. That would seem to qualify as a large genome, wouldn't it?

Right, that's that objection answered then!

mammuthus

Posts: 13
Joined: June 2009

By the way, all this genetic entropy (why the stupid name, why not just Muller's Ratchet?) stuff relates to the work of Laurence Loewe at Edinburgh.  He's done a lot of research on Muller's Ratchet, well worth checking out:

http://evolutionary-research.net/people/lloewe

also see these classic papers by Michael Lynch:

Lynch, M. et al. 1993. Mutational meltdowns in asexual populations. J. Heredity 84: 339-344

http://www.indiana.edu/~lynchlab/PDF/Lynch58.pdf

Gabriel, W. et al. 1993. Muller's ratchet and mutational meltdowns. Evolution 47: 1744-1757.

http://www.indiana.edu/~lynchlab/PDF/Lynch62.pdf

I'm not a population geneticist or indeed any kind of evolutionary biologist whatsoever.  But it's my impression that Sanford is saying nothing new; he's just trying to repackage issues that pop gen people have known about for decades.  Indeed, occasional creationist basher Joe Felsenstein published one of the classic papers in this respect:

Felsenstein, J. (1974). The Evolutionary Advantage of Recombination. Genetics, 78, 737–756

Some time ago on PandasThumb, Felsenstein said he'd probably better read the Sanford book as creationists would be using it.  S Cordova offered to send it to him.  It'd be great to get his thoughts.  I think this is the discussion:

http://pandasthumb.org/archives/2008/05/gamblers-ruin-i.html

mammuthus

Posts: 13
Joined: June 2009

Aaah yes, found it on the final page of comments:

 Quote Dr. Felsenstein,I sent you a copy of John Sanford’s Genetic Entropy.Let me know if you received it or not. The admins at PT should have my e-mail.Thank you again for taking time to read what I wrote at UD and for taking the time to respond. I’m deeply honored.regards, Salvador Cordova

 Quote Sorry for the delay, I didn’t notice this inquiry until recently. Yes, the book arrived. Thanks for sending it. It will be helpful to have it, I am sure.

Zachriel

Posts: 2560
Joined: Sep. 2006

Take a look at the distribution of beneficial mutations. {The parameters are as on the image and Maximal beneficial mutation effects = 0.1} Beneficial mutations spike, then disappear.

Generation 3970, Fitness 0.106, Deleterious 38398, Favorable 0.

The program doesn't seem to use my available memory and quits well before the specified generations. It doesn't seem to reseed the randomizer with each run.

Generation 3972, Fitness 0.101, Deleterious 38422, Favorable 0.

Just look at those graphs. That just doesn't look right at all.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

AnsgarSeraph

Posts: 11
Joined: June 2009

 Quote (Zachriel @ June 11 2009,20:41) The program doesn't seem to use my available memory and quits well before the specified generations.

With a fitness level at 0.1, I'm sure your populations went extinct. I can't keep populations below 1000 alive for very long; they certainly won't last for more than 20,000 generations.

—Sam

Zachriel

Posts: 2560
Joined: Sep. 2006

I manually changed the seed. This is what I got with the same parameters.

Generation 4376, Fitness 0.111, Deleterious 42350, Favorable, 0.

It's very odd having to change the seed every time. A common method of investigation is to rerun the same parameters to help distinguish trends from flukes.

There's something odd about the distribution. That might be due to the small population, though.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3091
Joined: May 2006

Quote (mammuthus @ June 11 2009,18:08)

 Quote (deadman_932 @ June 11 2009,15:57) Sanford's "genomic (mutational) meltdown" scenarios are a hoot. Even DaveScot was bright enough to see that Sanford's proposed mutation rates were out of line with reality: fast-reproducing sexual species that have existed a few million should have all been extinct by now, but they're not. Sanford inflates deleterious mutation rates and disregards compensatory mechanisms.

His argument is a little more involved than that.  It seems to revolve around genome size; the smaller genome size of something like P.falciparum prevents genetic meltdown, but it would occur with larger genome sized mammals.  So genetic entropy is a problem for the latter (if not on Sanford YEC timescales).  You can't just take fast reproducing things like P.falciparum and apply the Genetic Entropy failure in this case widely.  At least that's how I read it.

Well, Wes mentioned one example of "large" - genomed rapidly-reproducing species, and there's a lot more available. Mammal genomes average between 2 and 3 gigabases (Gb) but lots of insect and plant genomes (like wheat) can be larger:   around 16 Gb in wheat or grasshoppers (Podisma pedestris) -- five times larger than humans.

Nailing Sanford down on questions about interesting populations like california condors would be fun -- they're the only North American remnant of Gymnogyps, been around since the early Pleistocene and their population dropped down to 22 individuals not very long ago... and their est. genome size is 1.5 Gb. They should have accumulated enough deleterious mutations so that such a small closely-related group would produce nothin' but dead young, right? Or how about Przywalski's horse?

Sanford is a YEC of sorts, so he skewed his parameters to fit his skewed view of the Earth's entire biome being less than 100 K years old, as I recall ( I may be wrong with the exact figure there).

-------------------------------------------

ETA: I was curious about known recessives in the existing condors and there is one identified (chondrodystrophy) that results in fatal abnormalities  :

J. Geyer, O.A. Ryder, L.G. Chemnick and E.A. Thompson, Analysis of relatedness in the California condors: from DNA fingerprints, Mol. Biol. Evol. 10 (1993), pp. 571–589

Romanov MN, Koriabine M, Nefedov M, de Jong PJ, Ryder OA (2006) Construction of a California Condor BAC Library and First-generation Chicken-condor Comparative Physical Map as an Endangered Species Conservation Genomics Resource, Genomics, 88 (6), 711-8

--------------
AtBC Award for Thoroughness in the Face of Creationism

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote (Zachriel @ June 11 2009,20:41) Take a look at the distribution of beneficial mutations.

Looks right to me, given your parameters. You're getting 10 mutations/individual for 100 individuals, or 1000 mutations per generation. Of those, 1/100,000 is beneficial, so you're only getting one beneficial mutation every 100 generations. Those are the tiny blips. Once in a while one or two of them drift up to an appreciable, and the mean number of beneficial alleles per individual climbs above 1.0.

None of them fix though, which is not surprising, since they're almost all effectively neutral. Which means that you should have one fixing by chance every 20,000 generations, plus some probability from the tail at higher selection coefficient.

Occam's Aftershave

Posts: 1258
Joined: Feb. 2006

Over at TWeb where this started I asked the same question; why haven't all the fast reproducing mammal species died out from genetic meltdown yet?  The topic of mice was raised, because while mice have a genome roughly the size of humans  (approx. 3 GB), they have a generation time some 170x faster (6 weeks vs. 20 years).  So why haven't all the mice gone extinct by now?

I made the statement ""All other things being equal, the population that breeds faster will accumulate mutations faster."

Jorge Fernandez (a YEC who was acting as a go between to Sanford) supposedly forwarded my questions to Sanford and got this reply:

Sanford:  " No, it is just the opposite, short generation times means more frequent and better selective filtering."

Which makes zero sense and is trivially easy to refute with their own program:

Run Mendel with two populations that are identical in every way (i.e genome size, mutation rate, selection pressure, etc.) except make one generation time 2x the other, say two per year year vs. one per year.

If you run them both for 1000 generations, both will end up with the same (lower) fitness level, but the two per year will only take 500 years to get there.

If you run them both for 1000 years, the once per year will end up in the exact same fitness as the first trial, but the two per year will have 2000 generations and end up with an even lower fitness level, if it doesn't just go extinct first.

These guys are busted, and they know they're busted.  Now it's just a question of how far they can push this shit and how much money they can make before the errors become well known.

--------------
JoeG: And by eating the cake you are consuming the information- some stays with you and the rest is waste.

k.e..

Posts: 2498
Joined: May 2007

So they have gone from shining shit to simulating shit?

As a game strategy it could be a winner.

More obscurantism in the tard market makes it easier to collect loose fundy shekels.

--------------
"I get a strong breeze from my monitor every time k.e. puts on his clownDaveTard suit." dogdidit

Abbie Smith (ERV) who's got to be the most obnoxious arrogant snot I've ever seen except for when I look in a mirror. DAVE TARD

mammuthus

Posts: 13
Joined: June 2009

This new paper may be of interest:

 Quote Mustonen, V. and Lassig, M.  (2009) From fitness landscapes to seascapes: non-equilibrium dynamics of selection and adaptation.  Trends in Genetics, 25, 111-119.Evolution is a quest for innovation. Organisms adapt to changing natural selection by evolving new phenotypes. Can we read this dynamics in their genomes? Not every mutation under positive selection responds to a change in selection: beneficial changes also occur at evolutionary equilibrium, repairing previous deleterious changes and restoring existing functions. Adaptation, by contrast, is viewed here as a non-equilibrium phenomenon: the genomic response to time-dependent selection. Our approach extends the static concept of fitness landscapes to dynamic fitness seascapes. It shows that adaptation requires a surplus of beneficial substitutions over deleterious ones. Here, we focus on the evolution of yeast and Drosophila genomes, providing examples where adaptive evolution can and cannot be inferred, despite the presence of positive selection.

there's a section on Muller's Ratchet:

damitall

Posts: 251
Joined: Jan. 2009

Quote (mammuthus @ June 11 2009,18:08)

 Quote (deadman_932 @ June 11 2009,15:57) Sanford's "genomic (mutational) meltdown" scenarios are a hoot. Even DaveScot was bright enough to see that Sanford's proposed mutation rates were out of line with reality: fast-reproducing sexual species that have existed a few million should have all been extinct by now, but they're not. Sanford inflates deleterious mutation rates and disregards compensatory mechanisms.

His argument is a little more involved than that.  It seems to revolve around genome size; the smaller genome size of something like P.falciparum prevents genetic meltdown, but it would occur with larger genome sized mammals.  So genetic entropy is a problem for the latter (if not on Sanford YEC timescales).  You can't just take fast reproducing things like P.falciparum and apply the Genetic Entropy failure in this case widely.  At least that's how I read it.

 Quote It occured to me recently that Sanford’s projected rate of genetic decay doesn’t square with the observed performance of P.falciparum. P.falciparum’s genome is about 23 million nucleotides. At Sanford’s lowest given rate of nucleotide copy errors that means each individual P.falciparum should have, on average, about 3 nucleotide errors compared to its immediate parent. If those are nearly neutral but slightly deleterious mutations (as the vast majority of eukaryote mutations appear to be) then the number should be quite sufficient to cause a genetic meltdown from their accumulation over the course of billions of trillions of replications. Near neutral mutations are invisible to natural selection but the accumulation of same will eventually become selectable. If all individuals accumulate errors the result is decreasing fitness and natural selection will eventually kill every last individual (extinction). Yet P.falciparum clearly didn’t melt down but rather demonstrated an amazing ability to keep its genome perfectly intact. How?After thinking about it for a while I believe I found the answer - the widely given rate of eukaryote replication errors is correct. If P.falciparum individuals get an average DNA copy error rate of one in one billion nucleotides then it follows that approximately 97% of all replications result in a perfect copy of the parent genome. That’s accurate enough to keep a genome that size intact. An enviromental catastrophe such as an ice age which lowers temperatures even at the equator below the minimum of ~60F in which P.falciparum can survive would cause it to become extinct while genetic meltdown will not. Mammals however, with an average genome size 100 times that of P.falciparum, would have an average of 3 replication errors in each individual. Thus mammalian genomes would indeed be subject to genetic decay over a large number of generations which handily explains why the average length of time between emergence to extinction for mammals and other multicelled organisms with similar genome sizes is about 10 million years if the fossil and geological evidence paints an accurate picture of the past. I DO believe the fossil and geological records present us with an incontrovertible picture of progressive phenotype evolution that occured over a period of billions of years. I don’t disbelieve common ancestry and phenotype evolution by descent with modification - I question the assertion that random mutation is the ultimate source of modification which drove phylogenetic diversification.

Here is an abstract which might inform this particular question

Lou FCD

Posts: 5244
Joined: Jan. 2006

You've all forgotten the most important part of the simulation, and that's why your results are skewed.

You have to throw the computer off a cliff to get an accurate simulation.

duh.

--------------
Lou FCD is still in school, so we should only count him as a baby biologist. - carlsonjok - deprecated

I think I might love you. Don't tell Deadman - Wolfhound

Seduction by Louis, my NSFW new photography website.

Posts: 3091
Joined: May 2006

 Quote (Lou FCD @ June 12 2009,06:35) You've all forgotten the most important part of the simulation, and that's why your results are skewed.You have to throw the computer off a cliff to get an accurate simulation.duh.

Lou = absotively correckt. Heck, even checker-playing computers have to be painted in squares. Everyone knows that.

--------------
AtBC Award for Thoroughness in the Face of Creationism

mammuthus

Posts: 13
Joined: June 2009

Jorge Fernandez at TWeb is in contact with Sanford.  He just posted the following from Sanford:

 Quote Hi Jorge - I have been traveling ... The comment ... about "cooking the books" is, of course, a false accusation. The issue has to do with memory limits. Before a Mendel run starts it allocates the memory needed for different tasks. With deleterious mutations this is straight-forward - the upper range of mutation count is known. With beneficials it is harder to guess final mutation count - some beneficials can be vastly amplified. Where there is a high rate of beneficials they can quickly exhaust RAM and the run crashes. Wesley Brewer [one of the creators of Mendel] has tried to avoid this by placing certain limits - but fixing this is a secondary priority and will not happen right away. With more RAM we can do bigger experiments. It is just a RAM issue.Best - John

This is in response to - "Wes Elseberry made a comment that I think could be a good title, 'Mendel's Accountant
cooks the books."  I assume that they're talking about the failure of the program to increase fitness when a high number of beneficial mutations are specified.

I guess Sanford et al would argue that this problem isn't a big issue, since there's never a case in which there are loads (e.g. 90%) of beneficial mutations.  Deleterious or slightly deleterious are in the majority in reality, there's no RAM problem with these, and so the main conclusion they draw from Mendel is unaffacted by the problems shown with beneficial mutations.  At least I guess that's what he'd say.

Sanford also says:

 Quote The fact that our runs crash when we run out of RAM is not by design. If someone can help us solve this problem we would be very grateful. We typically need to track hundreds of millions of mutations. Beneficials create a problem for us because they amplify in number. We are doing the best we can.I would urge your colleagues [Heaven help me - John is under the impression that you people are my colleagues ... brrrrrrrr!] to use more care. In science we should be slow to raise claims of fraud without first talking to the scientist in question to get their perspective. Otherwise one might unwittingly be engaging in character assassination.

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

 Quote I guess Sanford et al would argue that this problem isn't a big issue, since there's never a case in which there are loads (e.g. 90%) of beneficial mutations.

No, the problem is quantitative and not qualitative. If the program doesn't handle the 90% case correctly, it isn't handling the 0.001% case correctly, either. And we know that v1.2.1 did not handle it correctly. If you are going around claiming to have produced an "accurate" simulation, you are on the hook for that.

The 90% case just makes the error blatantly obvious.

Speaking of hypocrisy, how careful is Sanford in not making sweeping generalizations about biologists having gotten things wrong?

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

As demonstrated in the two runs I did comparing the output of v1.2.1 and v1.4.1 on the very same configuration, v1.2.1 has a major error in its handling of beneficial mutations. This has nothing at all to do with memory limits; I also ran both with the default case, and the experimental case used in both merely changed the two parameters as specified by Zachriel above. The memory usage was under 130MB for all cases I ran; the memory I had was sufficient and the simulations ran to completion. Sanford either was given a garbled account of the issue or is deploying a meaningless digression as a response.

ETfix: 130,000KB = 130MB

Edited by Wesley R. Elsberry on June 12 2009,11:19

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

mammuthus

Posts: 13
Joined: June 2009

Quote (Wesley R. Elsberry @ June 12 2009,11:06)
 Quote I guess Sanford et al would argue that this problem isn't a big issue, since there's never a case in which there are loads (e.g. 90%) of beneficial mutations.

No, the problem is quantitative and not qualitative. If the program doesn't handle the 90% case correctly, it isn't handling the 0.001% case correctly, either. And we know that v1.2.1 did not handle it correctly. If you are going around claiming to have produced an "accurate" simulation, you are on the hook for that.

The 90% case just makes the error blatantly obvious.

Speaking of hypocrisy, how careful is Sanford in not making sweeping generalizations about biologists having gotten things wrong?

Ok, thanks Wesley.  I know nothing about programming, so a lot of what I have to say on realted subjects will be utter nonsense!.

I totally concur about Sanford's sweeping generalisations.  He claims that Mendel's Accountant has "falsified" Neo-Darwinian evolution:

 Quote When any reasonable set of biological parameters are used, Mendel provides overwhelming empirical evidence that all of the “fatal flaws” inherent in evolutionary genetic theory are real. This leaves evolutionary genetic theory effectively falsified—with a degree of certainty which should satisfy any reasonable and open-minded person.

and

 Quote As a consequence, evolutionary genetic theory now has no theoretical support—it is an indefensible scientific model. Rigorous analysis of evolutionary genetic theory consistently indicates that the entire enterprise is actually bankrupt. In this light, if science is to actually be self-correcting, geneticists must “come clean” and acknowledge the historical error, and must now embrace honest genetic accounting procedures.

http://www.icr.org/i....ory.pdf

I have zero respect for anyone who provides such rhetoric, without actually submitting their claims to review by the scientific community.  The very people they are lambasting.  That is fundamentally dishonest.

Sam at TWeb has emailed Sanford to see if he will engage directly at that messageboard.  Could be interesting.

mammuthus

Posts: 13
Joined: June 2009

Oh and an additional response from Sanford.  This is an explanation as to why such low population sizes (1000) were used and how this doesn't affect their conclusions.  In addition it's a response to the question of why mice (as an example of a pretty fast reproducing species) have not yet gone extinct.

 Quote Hi Jorge - Please tell these folks that I appreciate their interest in Mendel, and if they see certain ways we can make it more realistic, we will try and accommodate them.Mendel is fundamentally a research tool, and so offers a high degree of user-specification. There is no inherently "realistic" population size - it just depends on what circumstance you wish to study. The default setting for population size is set at 1000 because it is convenient - whether you are using the Windows version on your laptop, or any other computer, you are less likely to run out of memory. We are proceeding to study population size and also population sub-structure. I believe larger populations should realistically be set up as multiple tribes with a given migration rate between tribes. Under these conditions we see little improvement with larger population sizes. But they are welcome to do bigger runs if they have the memory resources.The mouse question is interesting. I think one would needto change various parameters for mouse - each species isdifferent. I would like to know the maximal (not minimal)generation time - do they know? This would define themaximal time to extinction. I have read that the pergeneration mutation rate is about an order of magnitudelower in mouse - which makes sense if there are fewer celldivisions in the generative cells between generations.I would be happy to do such experiments when I get theinput data.Best - John

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Does anyone know of an open-source UML system that takes FORTRAN code as input?

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Posts: 3091
Joined: May 2006

Quote (mammuthus @ June 12 2009,10:58)
Jorge Fernandez at TWeb is in contact with Sanford.  He just posted the following from Sanford:

 Quote Hi Jorge - I have been traveling ...The comment...about "cooking the books" is, of course, a false accusation. The issue has to do with memory limits. Before a Mendel run starts it allocates the memory needed for different tasks. With deleterious mutations this is straight-forward - the upper range of mutation count is known. With beneficials it is harder to guess final mutation count - some beneficials can be vastly amplified. Where there is a high rate of beneficials they can quickly exhaust RAM and the run crashes. Wesley Brewer [one of the creators of Mendel] has tried to avoid this by placing certain limits - but fixing this is a secondary priority and will not happen right away. With more RAM we can do bigger experiments. It is just a RAM issue.Best - John

This is in response to - "Wes Elseberry made a comment that I think could be a good title, 'Mendel's Accountant
cooks the books."  I assume that they're talking about the failure of the program to increase fitness when a high number of beneficial mutations are specified...
[snip]

Sanford also says:

 Quote "The fact that our runs crash when we run out of RAM is not by design. If someone can help us solve this problem we would be very grateful. We typically need to track hundreds of millions of mutations. Beneficials create a problem for us because they amplify in number. We are doing the best we can. I would urge your colleagues [Heaven help me - John is under the impression that you people are my colleagues ... brrrrrrrr!] to use more care. In science we should be slow to raise claims of fraud without first talking to the scientist in question to get their perspective. Otherwise one might unwittingly be engaging in character assassination."

http://www.theologyweb.com/campus....unt=131

That's interesting, because the 2008 ICR "Proceedings of the Sixth International Conference on Creationism (pp. 87–98)." Has a "paper" by John Baumgardner, John Sanford, Wesley Brewer, Paul Gibson and Wally Remine.

The title of that paper is  "Mendel’s Accountant: A New Population Genetics Simulation Tool for Studying Mutation and Natural Selection"  (.PDF link)

So what does John Sanford say there? Well, he says this:

 Quote Mendel  represents  an  advance  in  forward-time simulations by incorporating several improvements over previous simulation tools... Mendel is tuned for speed, efficiency and memory usage to handle large populations and high mutation rates....We  recognized that to track millions of individual mutations in a sizable population over many generations, effcient use of memory would be a critical issue – even with the large amount of memory commonly available on current generation computers. We therefore selected an approach that uses a single 32-bit (four-byte) integer to encode a mutation’s fitness effect, its location in the genome, and whether it is dominant or recessive. Using this approach, given 1.6 gigabytes of memory on a single microprocessor, we can accommodate at any one time some 400 million mutations...This implies that, at least in terms of memory, we can treat reasonably large cases using a single processor of the type found in many desktop computers today.

I await the actual achievement of these claims with un-bated breath. All emphases are mine.

--------------
AtBC Award for Thoroughness in the Face of Creationism

Posts: 3091
Joined: May 2006

 Quote (Wesley R. Elsberry @ June 12 2009,11:25) Does anyone know of an open-source UML system that takes FORTRAN code as input?

You might want to look through these: http://olex.openlogic.com/wazi....lopment

ETA:  Sorry, Nope, I can't find anything open-source... and I looked quite a bit at various fora, etc.

--------------
AtBC Award for Thoroughness in the Face of Creationism

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Acceleo seems to be able to generate FORTRAN from UML, but I'm looking for a free tool to generate UML from FORTRAN.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Mutations are not beneficial, neutral, or detrimental on their own, nor is their contribution to fitness fixed for all time. Mutations contribute to fitness in a context, and as the context changes, so may the value of its contribution to fitness. Fitness is a value that applies to the phenotype in ensemble. Mendel's Accountant appears instead to assume that mutations have a fixed value that cannot be changed by context. Thus, Mendel's Accountant appears to completely ignore research on compensatory mutations.

Because the value of a mutation depends on context, a particular mutation may be beneficial, neutral, or detrimental at initial appearance, but later become part of a different class as other mutations come into play. Mendel's Accountant treats mutations as falling into a fixed class.

These faults alone suffice to disqualify Mendel's Accountant from any claim to providing an accurate simulation of biological evolution.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Of course, I tend to think that a good approach to critique of a program to do a particular task is to actually produce a program that does that task better. I think that is something that we could give some thought to here. Much of the same background work applies to analysis of MA or design of an alternative.

Some ideas:

- Develop a test suite based on published popgen findings in parallel with development

- Base it on the most general, abstract principles for broad applicability

- Aim for number of generations to be limited only by amount of disk or other long-term storage available

- Consider means for handling large population sizes

- Start with a simple system, either as run-up to version 1 or with sufficient generality to be extensible to more complex systems

It seems to me that producing a thoroughly-vetted and tested platform that covers fewer cases is far better than producing a large, unwieldy, and bug-ridden product whose output cannot be trusted.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Bob O'H

Posts: 1865
Joined: Oct. 2005

 Quote I'm not a population geneticist or indeed any kind of evolutionary biologist whatsoever.  But it's my impression that Sanford is saying nothing new; he's just trying to repackage issues that pop gen people have known about for decades.

What's new is his claim that meltdown affects sexual populations.  I should check the evolution of sex literature, I'm sure they (Sally Otto and Nick Barton, amongst others) showed that it doesn't happen.  In his book Sanford ignores the recent evolution of sex literature.

Wes -
 Quote OK, why are there still Amoeba dubia around?

Indeed - hasn't it turned into Amoeba dubya?

Anyway, remember that Sanford is a YEC, so millions of years aren't relevant for him.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Dr.GH

Posts: 1864
Joined: May 2002

 Quote (Wesley R. Elsberry @ June 12 2009,14:28) Of course, I tend to think that a good approach to critique of a program to do a particular task is to actually produce a program that does that task better. I think that is something that we could give some thought to here. Much of the same background work applies to analysis of MA or design of an alternative. Some ideas:- Develop a test suite based on published popgen findings in parallel with development - Base it on the most general, abstract principles for broad applicability- Aim for number of generations to be limited only by amount of disk or other long-term storage available- Consider means for handling large population sizes- Start with a simple system, either as run-up to version 1 or with sufficient generality to be extensible to more complex systemsIt seems to me that producing a thoroughly-vetted and tested platform that covers fewer cases is far better than producing a large, unwieldy, and bug-ridden product whose output cannot be trusted.

Wes, How would your proposed project improve on other programs? For example, of the goals that you list, does existing software such as AVIDA or other models not already satisfy you criticisms?

Next, I see that there are two goals. The first is to refute lame ass creatocrap like "“Mendel's Accountant provides overwhelming empirical evidence that all of the "fatal flaws" inherent in evolutionary genetic theory are real. This leaves evolutionary genetic theory effectively falsified--with a degree of certainty that should satisfy any reasonable and open-minded person.”

The second would be to actually advance the scientific work of evo simulations.

I might be able to assist the first, and I am happy to leave the second to the rest of you.

Your list of ideas do add to the refutation of the creatocrap, as they are features of what a good simulator should be able to do.

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

Steve Schaffner

Posts: 13
Joined: June 2009

There may be some value in checking Mendel's Accountant, to see whether it really implements the model that it claims to, but I don't see much point in trying to cobble together a new program to simulate evolution here. That is a major research project, with many unknown parameters, i.e. a truly realistic simulation of evolution isn't possible yet.

The important questions about MA, assuming the program isn't simply fatally flawed, concern the model that it is implementing. For the default values, you don't have to run the program to know that it will produce genetic collapse of the population -- that's inevitable, given the assumptions of the model. The model assumes a large number of mildly deleterious mutations, so mild that they are unaffected by purifying selection. It also assumes purely hard selection, in which lower fitness translates directly into loss of fertility for the population, and few beneficial mutations (which are also of small effect), independent of the fitness of the population (i.e. no compensating mutations). Given those assumptions, the population will inevitably decline towards extinction, since there is no force counteracting the relentless accumulation of deleterious mutations. The model stands or falls on those assumptions; the code is a side-issue.

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

I'll check with Reed Cartwright about existing popgen packages.

Avida is not a package that aims to directly model biological population genetics. There is quite a lot of conceptual overlap between Avida evolution and biological evolution, but it isn't at the low-level that MA claims to operate at. For example, current research in Avida is looking at the role of compensatory mutations. But each mutation in an Avidian is an instruction, not a base as in DNA. The Avida research can provide another line of evidence that complements that of biological research on compensatory mutations, but it isn't aimed at answering questions like, "At what rate should we expect compensatory mutations to fix in species X?" It's that kind of question that the folks pushing MA position it as a tool to answer, or would if they took any note of compensatory mutations at all. I think you get the drift, though.

Avida, by the way, has no difficulty in cranking out data on generation after generation for a set population size. Most Avida work is done on population sizes between 900 and 10,000 Avidians. However, I'm working on extending the Avida-ED program, and one part is to allow up to 90,000 organisms in the population. That is, by the way, accomplished with a change to the graphical user interface to allow selection of a grid size of up to 300 by 300, where the current version's grid-size slider only goes up to 100. The underlying Avida instance is unchanged. Tracking mutations is possible in Avida as well, but is done in analysis after the run finishes. Runs can go into the millions of updates. I don't know that anyone has tried to find an upper limit. Avida's ability to do this is because it only needs to hold the current population and grid in memory. Everything else gets written to disk.

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Posts: 3138
Joined: Mar. 2008

Who woulda thunk a few years ago that entry level computers would have four gigs of memory, or that the retail price of four gigs would be about \$19.95?

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (Wesley R. Elsberry @ June 12 2009,23:07) I'll check with Reed Cartwright about existing popgen packages.Avida is not a package that aims to directly model biological population genetics. There is quite a lot of conceptual overlap between Avida evolution and biological evolution, but it isn't at the low-level that MA claims to operate at. For example, current research in Avida is looking at the role of compensatory mutations. But each mutation in an Avidian is an instruction, not a base as in DNA. The Avida research can provide another line of evidence that complements that of biological research on compensatory mutations, but it isn't aimed at answering questions like, "At what rate should we expect compensatory mutations to fix in species X?" It's that kind of question that the folks pushing MA position it as a tool to answer, or would if they took any note of compensatory mutations at all. I think you get the drift, though.Avida, by the way, has no difficulty in cranking out data on generation after generation for a set population size. Most Avida work is done on population sizes between 900 and 10,000 Avidians. However, I'm working on extending the Avida-ED program, and one part is to allow up to 90,000 organisms in the population. That is, by the way, accomplished with a change to the graphical user interface to allow selection of a grid size of up to 300 by 300, where the current version's grid-size slider only goes up to 100. The underlying Avida instance is unchanged. Tracking mutations is possible in Avida as well, but is done in analysis after the run finishes. Runs can go into the millions of updates. I don't know that anyone has tried to find an upper limit. Avida's ability to do this is because it only needs to hold the current population and grid in memory. Everything else gets written to disk.

Umm, what happened to Model-View-Controller? The idea that the model is costrained by the UI is pretty scary.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Wesley R. Elsberry

Posts: 4117
Joined: May 2002

Avida-ED is not meant to be how researchers usually use Avida. Avida-ED is a GUI interface aimed at making use of a limited set of Avida options easy for pedagogy. There are quite a number of simplifying restrictions that Avida-ED imposes for the runs that can be made with it, but there are still enough parameters under instructor or student control to allow a great many different experiments to be done with it.

Links to both Avida and Avida-ED via the Devolab

--------------
"You can't teach an old dogma new tricks." - Dorothy Parker

Steve Schaffner

Posts: 13
Joined: June 2009

There is a good list of forward simulation (as opposed to coalescent) programs here. The ones I'm familiar with (apart from MA) are intended to model the behavior of sequences and mutations, not the global evolution of species.

Dr.GH

Posts: 1864
Joined: May 2002

 Quote (Wesley R. Elsberry @ June 12 2009,14:28) - Develop a test suite based on published popgen findings in parallel with development

This seems to me to be an excellent way to test MA- known data parameters from known populations.

Also, what were the release dates for the different versions of MA compared to the different publication/conference dates?

Edited by Dr.GH on June 13 2009,07:24

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

Tracy P. Hamilton

Posts: 1186
Joined: May 2006

 Quote (Bob O'H @ June 12 2009,17:02) What's new is his claim that meltdown affects sexual populations.  I should check the evolution of sex literature, I'm sure they (Sally Otto and Nick Barton, amongst others) showed that it doesn't happen.  In his book Sanford ignores the recent evolution of sex literature.

Is Sanford practicing abstinence from the sex literature?

--------------
"Following what I just wrote about fitness, you’re taking refuge in what we see in the world."  PaV

"The hard undeniable reality is that nothing can move in spacetime, by definition!!" mapou

"The simple equation F = MA leads to the concept of four-dimensional space." GilDodgen

Zachriel

Posts: 2560
Joined: Sep. 2006

When each child has more than a single mutation, then average fitness can decrease over time. Even though fitness is a relative term, this decreasing fitness can impact essential biological mechanisms.

If the genome is 3e8 bases in size (or any such large number) and there is an average of one mutation per child, then we expect that ~1/3 of the children will *not* have mutations. If each mother produces 6 children, then chances are that each new generation will include many individuals without mutations. (E.g. mice often have several litters of 4-10 pups.)

If we use truncated selection, heritability=1, mutations=1, seed=30, all else default, this is what we see.

It's interesting to see how the deleterious mutations ride along with the beneficial mutations until fixation before being weeded out.

In nature, we expect that if the mother produces enough children, then there should be sufficient healthy progeny to prevent genetic meltdown and to allow the positive ratcheting of beneficial mutations. We might also expect that species will tend to push the envelope with regard to mutational limitations. That means when near the margins small changes in parameters will allow it tip one way or the other. With slow reproducers, we can then expect various factors that mitigate the long-term evolutionary trajectory with regard to the accumulation of deleterious mutations. One such factor is sexual selection which is prevalent in nearly all taxa of interest.

We can reasonably show that selection of a wide number of parameters avoids mutational meltdown and we would expect life to evolve to explore the limits of these parameters. Hence, to claim that a simplified simulation such as Mendel's Accountant can disprove evolution is not justified.

We're still rather curious about the selection parameters,

Truncation
Unrestricted probability
Strict proportionality probability
Partial truncation

i.e. exactly what each selection criterion is doing.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

Also curious about the "heritability" parameter.

 Quote Mendel's Accountant User Manual: Individual genetic fitness values are calculated based upon each individual’s total mutation inventory. Individual genetic fitness is defined as 1.0, adjusted by the positive and negative effects of all its mutations. To obtain phenotypic fitness the genetic fitness is modified using the specified heritability to account for nonheritable factors such as variations in the environment.

What is the other 80%? Randomness?

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Bob O'H

Posts: 1865
Joined: Oct. 2005

It should be.  I wonder how they calculate it, though.  The description makes it look like they calculate the genotypic variation in fitness, and then multiply it by 4 to get the environmental variance, and create a random environmental effect for each individual with that variance.  But that's nuts, because it means that the amount of environmental variation in fitness is directly tied to the amount of genetic variation.

it's possible, though, that they're doing nothing like this: the description is rather confused.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

mammuthus

Posts: 13
Joined: June 2009

More from Sanford.  Apparently, MA is the "state of the art" in numerical genetics. Also, he wants to collaborate with y'all. According to Jorge:

 Quote The last thing that JS did was ask me for the email address of the people performing these simulations & asking the questions so as to jointly work towards the goal of a more realistic & acceptable-by-all Mendel program. Collaborative science at it best.

anyway, here is his latest.  In italics are the points being responded to (from Steve Schaffner)
 Quote Dear Colleague - If we can make the program morerealistic, we will. Please explain what you would likedone ... How would you have us model soft selection?I fail to see why mutations should not causeextinction, especially given the additive model. As weapproach zero mean fitness, many individuals willhave a fitness of zero or less - we are forced totruncate them (if you are dead you should notrealistically reproduce), causing population size tostart to rapidly shrink. When there are less than twoindividuals, we consider the population extinct.2) The default value for the maximum beneficial value of mutations is much too low.Real-world estimates of positive selection coefficients for humans are in the rangeof 0.1, not 0.001.That is easily re-set, but one has to consider ifit is reasonable to realistically build up a genomeby increments of 10% (I am speaking of internalcomplexity - not adaptation to an externalenvironmental factor). I think that is like goingup Mt. Improbable using a helicopter.3) The starting population is genetically perfect, and all deviations from that stateincrease the chance of extinction. This does not accurately model an evolutionaryprocess, in which no population ever achieves perfection, merely adequacy.The fact that an ideal organism would have a major competitive advantagecompared to the real one does not imply that the real one is nonfunctional ordoomed to extinction. This is not a model of biological evolution.We do not assume an ideal starting genotype - weassume a uniform population after a populationbottleneck - with fitness set arbitrarily at 1.0.Finally, I also have a technical problem with the program as a software tool.It does not seem to be possible to run it indefinitely, nor have I seen any caseswhere it has even been able to run to equilibrium (or better, steady state).Whether that is because it continues to track mutations after they fix I don'tknow (that's my guess), but it means it is essentially useless as a research tool.It should be possible to simulate a population of size, say, 20,000 for 200,000generations. What would the memory requirements for that set of parameters be?Is the program really able to use the extra memory?We can turn off individual mutation tracking and justtrack the net fitness of each linkage block. We getnearly indeterminate processing - but we lose lots ofinteresting data. I would be happy to cooperate withyou - if you are interested. As far as I can determine,Mendel is now the "state of the art" in geneticnumerical simulation, and it improves everymonth. Are you aware of a better research platform?Best wishes - John Sanford

http://www.theologyweb.com/campus....unt=161

For full context, here is the material Sanford is responding too:
 Quote I haven't raised any claims of fraud, nor am I clamoring for an immediate response. I have the following problems with model, based on what I've seen here.1) There does not seem to be an option for true soft selection. Even if deleterious alleles do not affect fertility, they still cause the population to become extinct. This is not an accurate model of real genetics.2) The default value for the maximum beneficial value of mutations is much too low. Real-world estimates of positive selection coefficients for humans are in the range of 0.1, not 0.001.3) The starting population is genetically perfect, and all deviations from that state increase the chance of extinction. This does not accurately model an evolutionary process, in which no population ever achieves perfection, merely adequacy. The fact that an ideal organism would have a major competitive advantage compared to the real one does not imply that the real one is nonfunctional or doomed to extinction. This is not a model of biological evolution.Finally, I also have a technical problem with the program as a software tool. It does not seem to be possible to run it indefinitely, nor have I seen any cases where it has even been able to run to equilibrium (or better, steady state). Whether that is because it continues to track mutations after they fix I don't know (that's my guess), but it means it is essentially useless as a research tool. It should be possible to simulate a population of size, say, 20,000 for 200,000 generations. What would the memory requirements for that set of parameters be? Is the program really able to use the extra memory?If Sanford (or co-author) wishes to address these criticisms, I would welcome the response. As it stands, however, I do not see how one can use this model to make any statements about the likely behavior of evolving populations in the real world.

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote John Sanford: That is easily re-set, but one has to consider if it is reasonable to realistically build up a genome by increments of 10% (I am speaking of internal complexity - not adaptation to an external environmental factor). I think that is like going up Mt. Improbable using a helicopter.

Which goes to show that he doesn't understand his own simulation. Mendel's Accounant doesn't model "internal complexity". It purports to abstract selective differences.

A specific limit to beneficial mutations may not make sense. Some mutations may sweep over a population rapidly. It is certainly conceivable that a seemingly minor mutation could dramatically increase reproductive success, perhaps many-fold, such as when there is only one male that reproduces.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3138
Joined: Mar. 2008

There are two flavors of Creation Math:

Proving that observed phenomena are impossible, and proving that unobserved phenomena are inevitable.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Dr.GH

Posts: 1864
Joined: May 2002

 Quote (mammuthus @ June 15 2009,16:38) "More from Sanford.  Apparently, MA is the "state of the art" in numerical genetics. Also, he wants to collaborate with y'all. According to Jorge: ..."

This is interesting, but it is a wasted effort if filtered through Jorge F.

If Sanford wanted to, he would register at TWeb. I don't see the need to invite him here. But, that might be preferable to TWeb because we could be better assured of moderator/admin issues (ie. no need to bother filtering the JorgF crap).

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

sledgehammer

Posts: 529
Joined: Sep. 2008

Quote (Zachriel @ June 15 2009,19:00)

 Quote John Sanford: That is easily re-set, but one has to consider if it is reasonable to realistically build up a genome by increments of 10% (I am speaking of internal complexity - not adaptation to an external environmental factor). I think that is like going up Mt. Improbable using a helicopter.

Which goes to show that he doesn't understand his own simulation. Mendel's Accounant doesn't model "internal complexity". It purports to abstract selective differences.

A specific limit to beneficial mutations may not make sense. Some mutations may sweep over a population rapidly. It is certainly conceivable that a seemingly minor mutation could dramatically increase reproductive success, perhaps many-fold, such as when there is only one male that reproduces.

I think it is even worse that that Zach, for three reasons:

Firstly, the parameter in question is the "maximal fitness effect of a beneficial mutation". This has nothing to do with the physical makeup of the genome, as Dr Sanford seems to imply.  It seems absurd to link this parameter to some implication of "affected base pairs", much less some inferred "infusion of information" into the genome, as the description of this parameter in the MA manual below seems to imply:

 Quote Mendel's Accountant User Manual: Maximal beneficial mutation effects – A realistic upper limit must be placed upon beneficial mutations. This is because a single nucleotide change can expand total biological functionality of an organism only to a limited degree. The larger the genome and the greater the total genomic information, the less a single nucleotide is likely to increase the total. Researchers must make a judgment for themselves of what is a reasonable maximal value for a single base change. The MENDEL default value for this limit is 0.001. This limit implies that a single point mutation can increase total biological functionality by as much as 0.1%. In a genome such as man’s, assuming only 10% of the genome is functional, such a maximal impact point mutation might be viewed as equivalent to adding 300,000 new information-bearing base pairs each of which had the genome-wide average fitness contribution. Researchers need to honestly define the upper limit they feel is realistic for their species. However it should be obvious that, in all cases, the upper limit for beneficial mutation effects ought to correspond to a very small fraction of the total genomic information (i.e. a small number relative to one).

Call me cynical, but when this much handwaving is applied to a point,  I suspect it means that this is a crucial parameter when it comes to justifying the conclusion that "genetic entropy" leads inevitably to genetic meltdown.

Secondly, there seems to be no reason to limit the maximal fitness effect of a beneficial mutation to a very small number.  Clearly, as pointed out by Z and others, in a competitive environment, there seems to be no reason a single beneficial mutation cannot virtually guarantee reproductive success. In other words, why can't the maximal fitness benefit of a beneficial mutation be something close to unity?

Lastly, the parameter in question is implemented as a scale factor on the probability distribution of fitness effect, which, for no apparent reason, is hard coded to be identical to the shape of the PDF of fitness effects of deleterious mutations  (whose scale factor is hard coded to be -1, i.e. instant death).
The effect of this fitness effect PDF scaling is even more significant when one considers that the PDF shape is already heavily skewed so that the vast majority of mutations fall "under the radar" of selection. So now, all beneficial mutations are 1000-fold (default value 0.001) less likely to become fixed in the population through selection, hard or soft.  That seems unrealistic to me, so say the least.

To mitigate the above bias against beneficials,  I recommend setting the maximal beneficial fitness parameter close to unity, which will symmetrize the fitness effect probability distribution, and then play with the parameter that determines the proportion of beneficial mutations ( i.e. set the ratio of beneficial to deleterious mutations to .001 or whatever).
My guess is that this will "level the playing field" and have a significant effect on the overall fitness trend.

Maybe someone who has MA up and running (Zach, Sam?) could try this and report?

P.S. I occurs to me that the PDF shape symmetry might also help account for the effects of the mutational "flipping" of deleterious to beneficial and vice versa.

ETA clarification: "be fixed by selection" is now "become fixed in the population through selection"

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

JohnW

Posts: 1930
Joined: Aug. 2006

 Quote (sledgehammer @ June 16 2009,12:42) I think it is even worse that that Zach, for three reasons:

I think you're right, sledgehammer.  It looks like Sanford thinks the beneficial effect of a mutation is constrained by its "size" relative to the total size of the genome - the bigger the genome, the smaller the effect of a single mutation.

Assuming you and I are not misinterpreting, I think there are two possibilities:
(a) - he really is that dumb (perhaps he thinks giraffes have more neck genes than humans);
(b) - this is yet another silly exercise in apologetics - slosh a lot of sciency talk around, but fix it to make sure you get the answer Jesus wants you to get.

I'm strongly leaning (b).

--------------
It always amazes me to no end that people who find the Bible abhorent seem to focus only on the few instances where God commands a city destroyed.
- FTK

AnsgarSeraph

Posts: 11
Joined: June 2009

 Quote (sledgehammer @ June 16 2009,14:42) Maybe someone who has MA up and running (Zach, Sam?) could try this and report?

I was actually running simulations on this idea earlier today and will be posting some graphs up on TWeb within the next hour. The runs were identical, save that one run had a "maximal fitness effect" of 0.001 and the other a maximal fitness effect of 0.01. The first population, after ~6000 generations, had a fitness of 0.844. The second, after the same number of generations, had a fitness of 1.350. The number of beneficial mutations was cranked up to 75% to ensure the runs went for a significant number of generations.

I also received an e-mail from Dr. Sanford today, part of which dealt in this area . . . I am waiting to hear from him whether I have permission to forward his entire e-mail on forums; there wasn't any revelatory information, in any case. Dr. Sanford feels that 0.001 is an appropriate setting but is willing to discuss whether that is the case or not.

—Sam

sledgehammer

Posts: 529
Joined: Sep. 2008

Thanks Sam. Overall fitness switching from a negative trend to positive as a result of a 10X increase in "maximal beneficial fitness" seems to confirm that it is one of the most important parameters as far as the trend is concerned. Unfortunately, I can't see the attachments you posted on TWeb without registering.  Any chance you could post them here as well?  (Post them to my photobucket page, and then link them here via the image tag)

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote (Zachriel @ June 14 2009,08:47) If the genome is 3e8 bases in size (or any such large number) and there is an average of one mutation per child, then we expect that ~1/3 of the children will *not* have mutations. If each mother produces 6 children, then chances are that each new generation will include many individuals without mutations. (E.g. mice often have several litters of 4-10 pups.)

True, although that's probably not a good model for humans, who have something between 1 and 3 deleterious mutations (probably) per birth, and more likely close to the top end than the bottom of the range. That doesn't mean that the population has to collapse genetically. It just means that in the steady state, everyone is carrying a fair number of deleterious mutations, with those having the most being the least likely to reproduce.

 Quote If we use truncated selection, heritability=1, mutations=1, seed=30, all else default, this is what we see.

Note that the population survives only because of truncation selection, which is not a realistic process for such slightly deleterious mutations. In this model, each individual will have on average one new mutation with a negative selection coefficient of something like 10-6 or 10-7, but selection is nonetheless effective enough to perfectly sort the fitness of the individuals and eliminate only the least fit.

 Quote It's interesting to see how the deleterious mutations ride along with the beneficial mutations until fixation before being weeded out.

Yes. Selective sweeps in action.

AnsgarSeraph

Posts: 11
Joined: June 2009

I didn't see an option for me to add to your Photobucket page, so I registered and created my own.

Here are the files. The first two are fitness graphs:

These are the distribution graphs for JSW507 (deleterious first, beneficial second):

And 508:

I've uploaded all the pictures, as well as the Input and Output files to my Box.net account, also:

MENDEL Run - JSW507, JSW508

—Sam

Posts: 3138
Joined: Mar. 2008

 Quote It just means that in the steady state, everyone is carrying a fair number of deleterious mutations, with those having the most being the least likely to reproduce.

I'm having trouble coming to grips with the simple-mindedness of assuming that beneficial or deleterious mutations have any necessary effect at on on fecundity.

My family hosts a number of odd and relatively undesirable alleles. We tend to have extra sets of front teeth -- which requires surgery to prevent blocking permanent teeth. My son has Adam's missing rib, something that gets painful when playing racket sports. Most of us are nearsighted.

What we don't have are genes leading to early death or to belief in cult science. And based on anecdotal evidence, the inability to understand cumulative selection is positively correlated withing having nine children.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Steve Schaffner @ June 16 2009,18:54)

 Quote (Zachriel @ June 14 2009,08:47) If the genome is 3e8 bases in size (or any such large number) and there is an average of one mutation per child, then we expect that ~1/3 of the children will *not* have mutations. If each mother produces 6 children, then chances are that each new generation will include many individuals without mutations. (E.g. mice often have several litters of 4-10 pups.)

True, although that's probably not a good model for humans, who have something between 1 and 3 deleterious mutations (probably) per birth, and more likely close to the top end than the bottom of the range. That doesn't mean that the population has to collapse genetically. It just means that in the steady state, everyone is carrying a fair number of deleterious mutations, with those having the most being the least likely to reproduce.

It's probably not a good model for mice either. I'm trying a wide variety of parameters to determine the program's limits—or to break it. I've tried very high fecundity, high beneficial mutation rates, and various other extremes. What about additive vs. multiplicative mutational effects?

 Quote (AnsgarSeraph @ June 16 2009,19:00) Here are the files. The first two are fitness graphs:

Is that with maximum beneficial effect equal to one versus default? Fraction favorable is set very high.

Consider this. If we use the defaults, then no beneficial mutation becomes fixed, which is contrary to fact. Even if you believe species are ultimately doomed to genetic meltdown, it still means the default parameters or the program itself has a problem.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

AnsgarSeraph

Posts: 11
Joined: June 2009

 Quote (Zachriel @ June 16 2009,19:38) Is that with maximum beneficial effect equal to one versus default? Fraction favorable is set very high. Consider this. If we use the defaults, then no beneficial mutation becomes fixed, which is contrary to fact. Even if you believe species are ultimately doomed to genetic meltdown, it still means the default parameters or the program itself has a problem.

The first run (JSW507) has a maximum beneficial effect of 0.01, while the second run (JSW508) is set to the default 0.001. There's only one order of magnitude between the two runs.

Sanford seems to be willing to give on the accuracy of the default parameters for the program . . . which is odd, in that those are the parameters he defends as biologically realistic in his papers. I think there are enough wrong parameters (and possibly a completely wrong coding paradigm) that any couple of parameters could be fixed and the population would still show fitness decline.

At the moment, I'm leaning toward the idea that the program is broken . . . but hopefully we can get Dr. Sanford on TWeb soon enough and have some clear back-n-forth.

—Sam

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (AnsgarSeraph @ June 16 2009,19:51) At the moment, I'm leaning toward the idea that the program is broken . . . but hopefully we can get Dr. Sanford on TWeb soon enough and have some clear back-n-forth.—Sam

Every time they add a feature it seems to overlap another feature and reduce the effect of selection. "Probability selection" interacts with "Heritability". "Fraction of mutations beneficial" interacts with "maximum beneficial effect".

How does Mendel's Accountant handle "probability selection". If they use Roulette Wheel selection, then it seems to negate the number of offspring setting. Nor does Roulette Wheel selection have a problem maintaining the population as suggested in the manual.

I took a look at the source code, but it doesn't seem formatted for readability.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

mammuthus

Posts: 13
Joined: June 2009

The latest update is that Sanford will be engaged in a one on one discussion at TWeb with Steve Schaffner:

Quote
All right; I just finished responding back to Dr. Sanford. He appears to be willing to engage sfs1 in a discussion on TWeb. He agrees that a one-on-one discussion is best, since he prefers to keep the discussion from being polarized.

I told Dr. Sanford that I would initiate the process of setting up a Basketball Court thread but I'm not sure how to do that; any long-timers here know how that works? We could use this thread as a commentary thread, I think.

This has the potential to be really interesting. Dr. Sanford seems to be very genuine and polite; I think we've got a good chance to sort out some of MENDEL's problems without getting lost in the weeds.

Dr. Sanford permitted me to post our e-mail exchange; the emails are attached. This is the part that concerns MENDEL:

 Quote As you know, Mendel has enormous user-specificity. It is literally a genetic accounting program, and honestly takes the input parameters which the users chooses, and processes them through the biological mechanics of mutation, selection, meiosis, gamete fusion, and formation of the next generation.    The default settings are just a starting point for research. If you put in the right parameters you can get extreme evolution. However, we argue that realistic settings always yield degeneration. This can be a point of discussion.    In regrad to your own experiments, I would like to point out that    biologists realize that the distribution of good and bad mutations are not symetrical. There are far fewer beneficials, and the range of beneficials is different - it is generally acknowledged that beneficials have a lower mean effect (is is harder to make major improvements in a highly optimized system). If you go to the mutation specifications, you can specify a high maximal beneficial effect - even up to 1.0. The default is .001 - meaning that a maximal beneficial effect is small, increasing fitness by 0.1%. A setting of 1.0 means that a single mutation can double fitness - creating as much biological functionality as the entire rest of the genome. This type of setting has many biological and logical ramifications that require quite a bit of discussion. In an accounting program, a single mega-beneficial can ALWAYS compensate for any amount of genetic damage. But is that realistic?

A big thanks to sfs1 for agreeing to dialog with Dr. Sanford when he comes to TWeb. This should be an illuminating discussion and a great addition to the stuff we've already figured out.

—Sam

I think you'll have to register at TWeb to view the debate when it comes up online.

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (JohnW @ June 16 2009,15:47)

 Quote (sledgehammer @ June 16 2009,12:42) I think it is even worse that that Zach, for three reasons:

I think you're right, sledgehammer.  It looks like Sanford thinks the beneficial effect of a mutation is constrained by its "size" relative to the total size of the genome - the bigger the genome, the smaller the effect of a single mutation.

Assuming you and I are not misinterpreting, I think there are two possibilities:
(a) - he really is that dumb (perhaps he thinks giraffes have more neck genes than humans);
(b) - this is yet another silly exercise in apologetics - slosh a lot of sciency talk around, but fix it to make sure you get the answer Jesus wants you to get.

I'm strongly leaning (b).

He just said it again.

 Quote Sanford: A setting of 1.0 means that a single mutation can double fitness - creating as much biological functionality as the entire rest of the genome.

A doubling in fitness does not imply a doubling of "biological functionality". Resistance to plague doesn't imply a dramatic increase in the size of a genome. It may just mean that fleas think you smell bad.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

sledgehammer

Posts: 529
Joined: Sep. 2008

 Quote Sanford: A setting of 1.0 means that a single mutation can double fitness - creating as much biological functionality as the entire rest of the genome.

I don't buy that argument either.
This is from:
J. Sanford, J. Baumgardner, W. Brewer, P. Gibson, and W. Remine. Mendel's Accountant: A biologically realistic forward-time population genetics program. SCPE. 8(2), July 2007, pp. 147-165.

 Quote 3.2. Prescribing Fitness Effects of Mutations. ...These scale factors are meaningful relative to the initial fitness value assumed for the population before we introduce new mutations. In Mendel we assume this initial fitness value to be 1.0. For deleterious mutations, since lethal mutations exist, we choose dsf del = -1. For favorable mutations, we allow the user to specify the (positive) scale factor dsf fav. Normally, this would be a small value (e.g., 0.01 to 0.1), since it is only in very special situations that a single beneficial mutation wouldhave a very large effect.

Seems to me that if the scale factor for deleterious mutations of -1 represents lethality, (i.e. no chance of reproduction), then it's inverse for beneficial mutations, +1, would represent guaranteed reproductive success, not "doubling of fitness".

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (sledgehammer @ June 17 2009,16:39)

 Quote Sanford: A setting of 1.0 means that a single mutation can double fitness - creating as much biological functionality as the entire rest of the genome.

I don't buy that argument either.
This is from:
J. Sanford, J. Baumgardner, W. Brewer, P. Gibson, and W. Remine. Mendel's Accountant: A biologically realistic forward-time population genetics program. SCPE. 8(2), July 2007, pp. 147-165.

 Quote 3.2. Prescribing Fitness Effects of Mutations. ...These scale factors are meaningful relative to the initial fitness value assumed for the population before we introduce new mutations. In Mendel we assume this initial fitness value to be 1.0. For deleterious mutations, since lethal mutations exist, we choose dsf del = -1. For favorable mutations, we allow the user to specify the (positive) scale factor dsf fav. Normally, this would be a small value (e.g., 0.01 to 0.1), since it is only in very special situations that a single beneficial mutation wouldhave a very large effect.

Seems to me that if the scale factor for deleterious mutations of -1 represents lethality, (i.e. no chance of reproduction), then it's inverse for beneficial mutations, +1, would represent guaranteed reproductive success, not "doubling of fitness".

Relative fitness compares different genotypes in a population, and is defined as the ratio of the average numbers contributed to the next generation with one genotype set arbitarily at 1. So if genotype-A contributes 300 and normal genotype-B contributes 200, then genotype-A has a relative fitness of 1.5 compared to genotype-B. Relative fitness can be most any non-negative number.

Absolute fitness is calculated for a single genotype as simply the ratio of the numbers in the new generation to the old after selection. So if the population of the genotype increases from 100 to 200, then it has an absolute fitness of 2. Again, absolute fitness can be most any non-negative number.

I've been trying to independently implement Mendel's Accountant, but keep running into such definitional problems. Heritability. Fitness. And how they're handling probability selection. I'm working with a simplified model, but Mendel's Accountant should be able to handle the simple cases with obvious results.

I'm assuming that if fitness increases by 1, then it goes from 1 to 2 (100% increase), or from 2 to 3 (50%) and so on. It shouldn't be additive, but multiplicative so it scales. Sanford's complaint is that if we use multiplicative, then it can never reach zero. So he is clearly assuming his conclusion.

It's not easy to resolve some of these problems. If we scale fecundity with fitness, then that solves the problem of very low fitness. But introduces a problem if the fitness levels climb so that we may be radically multiplying the reproductive rate.

Of course, "generation" is an abstraction, so it may represent an undefined breeding season. Frankly, the whole thing is an abstraction, so any strong claims about the specifics of biology are invalid anyway.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Henry J

Posts: 3735
Joined: Mar. 2005

 Quote I took a look at the source code, but it doesn't seem formatted for readability.

Maybe it evolved?

Henry

Zachriel

Posts: 2560
Joined: Sep. 2006

Some bits from Mendel's Accountant source code.

Offspring:

if(fitness_dependent_fertility) then

Favorable Mutations:

c...  Compute mean absolute fitness effect for favorable mutations.

sum = 0.
d2  = 1.

do i=1,1000000
d1 = d2
d2 = exp(-alpha_fav*(0.000001*i)**gamma_fav)
sum = sum + d1 + d2
end do

fav_mean = 0.0000005*sum*max_fav_fitness_gain

Phenotypic Fitness:

noise = sqrt(geno_fitness_variance*(1. - heritability) /heritability + non_scaling_noise**2)

c...  Add noise to the fitness to create a phenotypic fitness score...
do i=1,total_offspring
pheno_fitness(i) = fitness(i) + random_normal()*noise

Unrestricted probability selection:

c...     For unrestricted probability selection, divide the phenotypic
c...     fitness by a uniformly distributed random number prior to
c...     ranking and truncation.  This procedure allows the probability
c...     of surviving and reproducing in the next generation to be
c...     directly related to phenotypic fitness and also for the correct
c...     number of individuals to be eliminated to maintain a constant
c...     population size.

do i=1,total_offspring
work_fitness(i) = work_fitness(i)/(randomnum(1) + 1.d-15)
end do

Divide by randomnum as well as add non-heritable noise to the phenotype?

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Bob O'H

Posts: 1865
Joined: Oct. 2005

 Quote (Zachriel @ June 17 2009,19:39) Relative fitness compares different genotypes in a population, and is defined as the ratio of the average numbers contributed to the next generation with one genotype set arbitarily at 1. So if genotype-A contributes 300 and normal genotype-B contributes 200, then genotype-A has a relative fitness of 1.5 compared to genotype-B. Relative fitness can be most any non-negative number. Absolute fitness is calculated for a single genotype as simply the ratio of the numbers in the new generation to the old after selection. So if the population of the genotype increases from 100 to 200, then it has an absolute fitness of 2. Again, absolute fitness can be most any non-negative number.

Yes, this is how it's done.  Personally, I'd prefer it if it was on the log scale: there's all sorts of statistical theory that slots nicely into the evolutionary theory.

 Quote I've been trying to independently implement Mendel's Accountant, but keep running into such definitional problems. Heritability. Fitness. And how they're handling probability selection. I'm working with a simplified model, but Mendel's Accountant should be able to handle the simple cases with obvious results.

My advice: keep away from heritability.  It complicates matters, and is dependent on the genetic variation in the population.  I suspect Sanford et al. don't really understand quantitative genetics: certainly Sanford makes some mistakes because of his lack of understanding in Genetic Entropy.

 Quote I'm assuming that if fitness increases by 1, then it goes from 1 to 2 (100% increase), or from 2 to 3 (50%) and so on. It shouldn't be additive, but multiplicative so it scales. Sanford's complaint is that if we use multiplicative, then it can never reach zero. So he is clearly assuming his conclusion.

Indeed, but it can get arbitrarily close to 0, so it doesn't make any practical difference (unless you're working with continuous populations, when you end up with nano-foxes).

 Quote It's not easy to resolve some of these problems. If we scale fecundity with fitness, then that solves the problem of very low fitness. But introduces a problem if the fitness levels climb so that we may be radically multiplying the reproductive rate.

Don't you just have to invoke density dependence?  I think this Darwin chap had some thoughts along those lines, after he read Malthus.

 Quote Of course, "generation" is an abstraction, so it may represent an undefined breeding season. Frankly, the whole thing is an abstraction, so any strong claims about the specifics of biology are invalid anyway.

Depends on what species you're working on.  For things like butterflies, it's fine.  And, to be honest, the purpose of Mendel's Accountant is to make general statements about evolution, so this stuff is OK, as long as it's clear what the assumptions are.  A lot of the assumptions shouldn't have too big an effect on the robustness of the claims.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Bob O'H

Posts: 1865
Joined: Oct. 2005

 Quote (Zachriel @ June 17 2009,20:59) Some bits from Mendel's Accountant source code. Offspring: if(fitness_dependent_fertility) then               fitness_adjusted_offspring = num_offspring*sqrt(post_sel_fitness)

Any idea why the square root of post_sel_fitness?

 Quote Favorable Mutations: c...  Compute mean absolute fitness effect for favorable mutations.      sum = 0.      d2  = 1.      do i=1,1000000         d1 = d2         d2 = exp(-alpha_fav*(0.000001*i)**gamma_fav)         sum = sum + d1 + d2      end do      fav_mean = 0.0000005*sum*max_fav_fitness_gain

Ugh.  That's a horrible way to do the integration.  I recognise the density (George Box was promoting it in the 50s), and it has an analytic solution: alpha_fav*gamma_fav*Gamma(1/gamma_fav), where Gamma() is the gamma function.

(ref: Box, G. E. P. 1953. A note on regions for tests of kurtosis. Biometrika 40: 465-468)

Also, where does the 0.0000005 come from?  I'm always suspicious of constants like that.

 Quote Phenotypic Fitness:     noise = sqrt(geno_fitness_variance*(1. - heritability) /heritability + non_scaling_noise**2) c...  Add noise to the fitness to create a phenotypic fitness score... do i=1,total_offspring         pheno_fitness(i) = fitness(i) + random_normal()*noise

The random_normal()*noise[/color] is environmental variation, and we would typically set it to be constant.  Because MA defines heritability, they have to back-calculate the environmental variance: that's what geno_fitness_variance*(1. - heritability) /heritability is doing.  Except it's wrong, because the non_scaling_noise is added too, so the heritability isn't a heritability.

Also, note what this back-calculation means: it scales the environmental variance to the genetic variance, so as genetic variation decreases, the environment becomes more stable.  This is bollocks.

They should really set the environmental variance.  The problem is getting an easily understood scaling, but that can be done by scaling to the mean fitness effects.

Also, check through: the calculations look OK for log(fitness): they range from -infinity to infinity and are additive.  But on what scale does pheno_fitness affect?  On this scale, or the exponential scale?

 Quote Unrestricted probability selection:c...     For unrestricted probability selection, divide the phenotypic  c...     fitness by a uniformly distributed random number prior to c...     ranking and truncation.  This procedure allows the probability c...     of surviving and reproducing in the next generation to bec...     directly related to phenotypic fitness and also for the correctc...     number of individuals to be eliminated to maintain a constantc...     population size.         do i=1,total_offspring            work_fitness(i) = work_fitness(i)/(randomnum(1) + 1.d-15)         end doDivide by randomnum as well as add non-heritable noise to the phenotype?

:-)  No idea what's going on.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Bob O'H @ June 18 2009,01:30)

 Quote (Zachriel @ June 17 2009,19:39) I've been trying to independently implement Mendel's Accountant, but keep running into such definitional problems. Heritability. Fitness. And how they're handling probability selection. I'm working with a simplified model, but Mendel's Accountant should be able to handle the simple cases with obvious results.

My advice: keep away from heritability.  It complicates matters, and is dependent on the genetic variation in the population.  I suspect Sanford et al. don't really understand quantitative genetics: certainly Sanford makes some mistakes because of his lack of understanding in Genetic Entropy.

I've been setting heritability to 1 when running tests, so I should be able to reach comparative results. I think a working heritability parameter could be included in a Mendel's Accountant.  But the concept is sometimes counterintuitive.

Quote (Bob O'H @ June 18 2009,01:30)

 Quote (Zachriel @ June 17 2009,19:39) Of course, "generation" is an abstraction, so it may represent an undefined breeding season. Frankly, the whole thing is an abstraction, so any strong claims about the specifics of biology are invalid anyway.

Depends on what species you're working on.  For things like butterflies, it's fine.  And, to be honest, the purpose of Mendel's Accountant is to make general statements about evolution, so this stuff is OK, as long as it's clear what the assumptions are.  A lot of the assumptions shouldn't have too big an effect on the robustness of the claims.

I think you could make claims about particular models of evolution, but you would have to be very careful about a bold claim that evolution is falsified based on such an abstraction. Certainly some carefully devised claims can be justified.

Quote (Bob O'H @ June 18 2009,01:58)

 Quote (Zachriel @ June 17 2009,20:59) Some bits from Mendel's Accountant source code. Offspring: if(fitness_dependent_fertility) then               fitness_adjusted_offspring = num_offspring*sqrt(post_sel_fitness)

Any idea why the square root of post_sel_fitness?

I'm guessing it's arbitrary—to keep the reproductive rate from running away.

 Quote (Bob O'H @ June 18 2009,01:58) Also, where does the 0.0000005 come from?  I'm always suspicious of constants like that.

I was kinda hoping you knew. It may be an artifact of the calculation, but why that particular number? I agree that having a constant imbedded in the code without a notation is rather odd. Even if it is a standard equation, it's still best to notate it.

Quote (Bob O'H @ June 18 2009,01:58)

 Quote (Zachriel @ June 17 2009,20:59) Phenotypic Fitness:    noise = sqrt(geno_fitness_variance*(1. - heritability) /heritability + non_scaling_noise**2) c...  Add noise to the fitness to create a phenotypic fitness score... do i=1,total_offspring         pheno_fitness(i) = fitness(i) + random_normal()*noise

The random_normal()*noise[/color] is environmental variation, and we would typically set it to be constant.  Because MA defines heritability, they have to back-calculate the environmental variance: that's what geno_fitness_variance*(1. - heritability) /heritability is doing.  Except it's wrong, because the non_scaling_noise is added too, so the heritability isn't a heritability.

Non-scaling noise is set to 0 in the parameters, so we can ignore that for now. The problem of environmental variance scaling with genetic variance is a serious problem. Who could have guessed that the dinosaurs could have avoided planetary oblivion if they just weren't so darned diverse.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Zachriel @ June 18 2009,07:02)
Quote (Bob O'H @ June 18 2009,01:30)

 Quote (Zachriel @ June 17 2009,19:39) I've been trying to independently implement Mendel's Accountant, but keep running into such definitional problems. Heritability. Fitness. And how they're handling probability selection. I'm working with a simplified model, but Mendel's Accountant should be able to handle the simple cases with obvious results.

My advice: keep away from heritability.  It complicates matters, and is dependent on the genetic variation in the population.  I suspect Sanford et al. don't really understand quantitative genetics: certainly Sanford makes some mistakes because of his lack of understanding in Genetic Entropy.

I've been setting heritability to 1 when running tests, so I should be able to reach comparative results. I think a working heritability parameter could be included in a Mendel's Accountant.  But the concept is sometimes counterintuitive.

We only need to be concerned with ranking, not absolute phenotypic fitness, when simulating heritability. So I've been normalizing fitness (which preserves ranking), then applying the specified noise. For a child population of 25 and heritability of 50%, I get rankings like this. The number is genotypic rank, the position is phenotypic rank.

3
5
1
8
6
2
9
7
12
15
4
13
11
10
22
19
16
14
18
20
21
24
23
17
25

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

sledgehammer

Posts: 529
Joined: Sep. 2008

Quote (Bob O'H @ June 17 2009,23:58)

 Quote Favorable Mutations: c...  Compute mean absolute fitness effect for favorable mutations.      sum = 0.      d2  = 1.      do i=1,1000000         d1 = d2         d2 = exp(-alpha_fav*(0.000001*i)**gamma_fav)         sum = sum + d1 + d2      end do      fav_mean = 0.0000005*sum*max_fav_fitness_gain

Ugh.  That's a horrible way to do the integration.  I recognise the density (George Box was promoting it in the 50s), and it has an analytic solution: alpha_fav*gamma_fav*Gamma(1/gamma_fav), where Gamma() is the gamma function.

(ref: Box, G. E. P. 1953. A note on regions for tests of kurtosis. Biometrika 40: 465-468)

Also, where does the 0.0000005 come from?  I'm always suspicious of constants like that.

Well it is 1/2*1/1,000,000, and since it is a density, the sum-as-integral needs to be scaled, but I thought the (0.000001*i) was supposed to do that.
I think it's a boo-boo.

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

Zachriel

Posts: 2560
Joined: Sep. 2006

Nevermind.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Dr.GH

Posts: 1864
Joined: May 2002

 Quote (Zachriel @ June 18 2009,11:13) Nevermind.

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

k.e..

Posts: 2498
Joined: May 2007

Quote (Dr.GH @ June 18 2009,22:28)
 Quote (Zachriel @ June 18 2009,11:13) Nevermind.

phhhht

You say statistics I say stochastics.

Mathmaticians provide the sheet music, I just want the finger positions and the beat.

Actually screw taht, I think I'll just bit torrent the whole damn lot.

If those turkeys can design a better mouse tarp(sic) using that horse and buggy with square wheels then all our moons are their bases.

It aint going to happen, not now not eva.

Look out for the patent. It will be up there with zero wavelenght quantum antigravity decelarators.
Selling webverts and elephnat(sic) repellant is about all they can do.

--------------
"I get a strong breeze from my monitor every time k.e. puts on his clownDaveTard suit." dogdidit

Abbie Smith (ERV) who's got to be the most obnoxious arrogant snot I've ever seen except for when I look in a mirror. DAVE TARD

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote (Bob O'H @ June 18 2009,01:30) My advice: keep away from heritability.  It complicates matters, and is dependent on the genetic variation in the population.  I suspect Sanford et al. don't really understand quantitative genetics: certainly Sanford makes some mistakes because of his lack of understanding in Genetic Entropy.

Modeling is easier if you simply work with the selective advantage of the genotype, rather than the selection coefficient for a partly heritable trait. Here the partly heritable trait is fitness itself, which makes my head hurt.

 Quote Indeed, but it can get arbitrarily close to 0, so it doesn't make any practical difference (unless you're working with continuous populations, when you end up with nano-foxes).

What kind of fitness are we talking about here, though? Since MA keeps the population constant, it is implicitly using relative fitness. In that case, introducing an arbitrary scaling factor into the fitness doesn't matter; it's only the ratio of fitnesses that matters. It seems to me that the model treats fitness as being relative until it get very small, at which point it is treated as absolute. But there is no simple way to determine absolute fitness from relative fitness.

This seems like a basic point, but I don't understand what the program is trying to model here.

sledgehammer

Posts: 529
Joined: Sep. 2008

 Quote (Steve Schaffner @ June 18 2009,14:59) Since MA keeps the population constant, it is implicitly using relative fitness. In that case, introducing an arbitrary scaling factor into the fitness doesn't matter; it's only the ratio of fitnesses that matters. It seems to me that the model treats fitness as being relative until it get very small, at which point it is treated as absolute. But there is no simple way to determine absolute fitness from relative fitness. This seems like a basic point, but I don't understand what the program is trying to model here.

[Cynic]
I think the program is trying to model nothing more or less than the complete, abject failure of Darwinian Evolution to produce anything other than Genetic Entropy leading inevitably to Mutational Meltdown, unless the genome is infused with Complex Specified Information from a divine source.
[/Cynic]

--------------
The majority of the stupid is invincible and guaranteed for all time. The terror of their tyranny is alleviated by their lack of consistency. -A. Einstein  (H/T, JAD)
If evolution is true, you could not know that it's true because your brain is nothing but chemicals. ?Think about that. -K. Hovind

Posts: 3091
Joined: May 2006

Quote (sledgehammer @ June 18 2009,17:36)
 Quote (Steve Schaffner @ June 18 2009,14:59) Since MA keeps the population constant, it is implicitly using relative fitness. In that case, introducing an arbitrary scaling factor into the fitness doesn't matter; it's only the ratio of fitnesses that matters. It seems to me that the model treats fitness as being relative until it get very small, at which point it is treated as absolute. But there is no simple way to determine absolute fitness from relative fitness. This seems like a basic point, but I don't understand what the program is trying to model here.

[Cynic]
I think the program is trying to model nothing more or less than the complete, abject failure of Darwinian Evolution to produce anything other than Genetic Entropy leading inevitably to Mutational Meltdown, unless the genome is infused with Complex Specified Information from a divine source.
[/Cynic]

The answer is quite clear, materialist chance-worshipping Darweenieans:

--------------
AtBC Award for Thoroughness in the Face of Creationism

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Bob O'H @ June 18 2009,01:58)

 Quote Unrestricted probability selection:c...     For unrestricted probability selection, divide the phenotypic  c...     fitness by a uniformly distributed random number prior to c...     ranking and truncation.  This procedure allows the probability c...     of surviving and reproducing in the next generation to bec...     directly related to phenotypic fitness and also for the correctc...     number of individuals to be eliminated to maintain a constantc...     population size.         do i=1,total_offspring            work_fitness(i) = work_fitness(i)/(randomnum(1) + 1.d-15)         end doDivide by randomnum as well as add non-heritable noise to the phenotype?

:-)  No idea what's going on.

Basically, he is applying reductions in heritability twice. The heritability function itself, and then this random procedure for selecting reproductive winners by re-ranking them before truncation and passing to the next generation. We could modify the divisor to some function(randomnum) and adjust the degree and type of randomness for picking winners {something like randomnum^N}. It's just another way of introducing random factors into the choice of winners and losers which should already have been accounted for in the heritability function.

The net result is a significant reduction in the effect of selection.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote Basically, he is applying reductions in heritability twice. The heritability function itself, and then this random procedure for selecting reproductive winners by re-ranking them before truncation and passing to the next generation. We could modify the divisor to some function(randomnum) and adjust the degree and type of randomness for picking winners {something like randomnum^N}. It's just another way of introducing random factors into the choice of winners and losers which should already have been accounted for in the heritability function. The net result is a significant reduction in the effect of selection.

It would make more sense if it were described in terms of some other phenotype with an effect on fitness. The phenotype has a genetic component and an environmental (or random) component, i.e. has a heritability. The phenotype then confers a fitness, which is the probability of successful reproduction. The number of successful offspring is also drawn from a random distribution, which is what's being done in this bit of code (I guess treated as a binomial distribution).

As a model of selection that seems reasonable (apart from the way the noise scales), but expressing it in terms of the heritability of fitness I find hard to understand -- fitness isn't a phenotype, it's a measure of the success of a phenotype. And the whole thing is pretty convoluted, when the essence of the model could be captured simply by assigning a fitness to the genotype and then calculating the number of offspring. This is a model of evolution written by a breeder rather than by a population geneticist, I would say.

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Steve Schaffner @ June 18 2009,20:53)
 Quote Basically, he is applying reductions in heritability twice. The heritability function itself, and then this random procedure for selecting reproductive winners by re-ranking them before truncation and passing to the next generation. We could modify the divisor to some function(randomnum) and adjust the degree and type of randomness for picking winners {something like randomnum^N}. It's just another way of introducing random factors into the choice of winners and losers which should already have been accounted for in the heritability function. The net result is a significant reduction in the effect of selection.

It would make more sense if it were described in terms of some other phenotype with an effect on fitness. The phenotype has a genetic component and an environmental (or random) component, i.e. has a heritability. The phenotype then confers a fitness, which is the probability of successful reproduction. The number of successful offspring is also drawn from a random distribution, which is what's being done in this bit of code (I guess treated as a binomial distribution).

Yes, I'm okay with that. That's how my own (rather primitive) model is structured. I'm not modeling recombination at this point, because I don't think that's where the problem lies. It's more basic than that. There's seems to be very little signal of selection. I'm still tinkering, but his results don't seem to jive.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3138
Joined: Mar. 2008

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Zachriel

Posts: 2560
Joined: Sep. 2006

Phenotypic Fitness:

noise = sqrt(geno_fitness_variance*(1. - heritability) /heritability + non_scaling_noise**2)

c...  Add noise to the fitness to create a phenotypic fitness score...
do i=1,total_offspring
pheno_fitness(i) = fitness(i) + random_normal()*noise

Leaving aside the non-scaling noise which defaults to zero. And leaving aside the scaling problem which can be overcome with normalization.

c...     For unrestricted probability selection, divide the phenotypic
c...     fitness by a uniformly distributed random number prior to
c...     ranking and truncation.  This procedure allows the probability
c...     of surviving and reproducing in the next generation to be
c...     directly related to phenotypic fitness and also for the correct
c...     number of individuals to be eliminated to maintain a constant
c...     population size.

do i=1,total_offspring
work_fitness(i) = work_fitness(i)/(randomnum(1) + 1.d-15)
end do

The first random factor represents non-heritable influences on phenotype, the second representing chance of successful reproduction. Is there any processing between these two functions that we can't simply reduced to (ignoring the infinitesimal):

pheno_fitness(i) = (fitness(i) + random_normal()*noise)    /  randomnum(1)?

Working fitness being just a copy of phenotypic fitness that is only used for ranking before truncation.

c...  Copy the phenotypic fitnesses into array work_fitness.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Bob O'H

Posts: 1865
Joined: Oct. 2005

 Quote (Steve Schaffner @ June 18 2009,20:53) It would make more sense if it were described in terms of some other phenotype with an effect on fitness. The phenotype has a genetic component and an environmental (or random) component, i.e. has a heritability. The phenotype then confers a fitness, which is the probability of successful reproduction. The number of successful offspring is also drawn from a random distribution, which is what's being done in this bit of code (I guess treated as a binomial distribution).

The problem with describing the model in terms of a phenotype is that you then have to map the effect of the phenotype onto fitness.  It's easier just to leap in and model fitness directly.  Everything works fine if you define fitness as proportional to the expected number of offspring produced by an individual.

I haven't looked at the code, but if we assuming a constant population size and discrete generations, then (ignoring recombination and mutation), the way to model this is to assume that each parent has a fitness si.  The offspring are then drawn from a multinomial distribution with probability for the ith parent being

si/sum(si)

(this would reduce to a binomial distribution if there were only 2 parents).  The multinomial sampling is genetic drift.

You can treat log(si) as you would any standard trait: it's additive, so you can add the genetic and environmental effects.

 Quote As a model of selection that seems reasonable (apart from the way the noise scales), but expressing it in terms of the heritability of fitness I find hard to understand -- fitness isn't a phenotype, it's a measure of the success of a phenotype. And the whole thing is pretty convoluted, when the essence of the model could be captured simply by assigning a fitness to the genotype and then calculating the number of offspring. This is a model of evolution written by a breeder rather than by a population geneticist, I would say.

Not even a breeder: I would expect them to understand heritability a bit better than this!

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Dr.GH

Posts: 1864
Joined: May 2002

Reading the discussion has made me reconsider Wesley's earlier suggestion that you guys build a better program. It might be a way of collecting your observations.

However, if "Mendle's Accountant Cooked the Books," Sanford et al will merely claim that your new program has predetermined its answer.

--------------
"Science is the horse that pulls the cart of philosophy."

L. Susskind, 2004 "SMOLIN VS. SUSSKIND: THE ANTHROPIC PRINCIPLE"

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote (Bob O'H @ June 19 2009,12:02) I haven't looked at the code, but if we assuming a constant population size and discrete generations, then (ignoring recombination and mutation), the way to model this is to assume that each parent has a fitness si.  The offspring are then drawn from a multinomial distribution with probability for the ith parent being si/sum(si)(this would reduce to a binomial distribution if there were only 2 parents).  The multinomial sampling is genetic drift.

Yes, I've written a program for that kind of model, except that I imposed selection in the differential survival of the offspring. Including options for truncation selection and a few other things, it amounted to all of 158 lines, including comments, white space and the multinomial routine.

 Quote You can treat log(si) as you would any standard trait: it's additive, so you can add the genetic and environmental effects.

slpage

Posts: 349
Joined: June 2004

 Quote (Dr.GH @ June 19 2009,12:50) Reading the discussion has made me reconsider Wesley's earlier suggestion that you guys build a better program. It might be a way of collecting your observations.However, if "Mendle's Accountant Cooked the Books," Sanford et al will merely claim that your new program has predetermined its answer.

And from what I have seen, he would then just be projecting.

I am still wondering why they think that constraining the outcomes to a constant population size is biolgically realistic.

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote (slpage @ June 19 2009,13:09) I am still wondering why they think that constraining the outcomes to a constant population size is biolgically realistic.

It's a feature of many population genetics models. It has the advantage of being simple. How accurate it is depends a lot on what organism you're looking at.

Of course, there's a big difference between using models to analyze how particular aspects of evolution work and trying to model the entire process well enough to say whether it can occur.

Henry J

Posts: 3735
Joined: Mar. 2005

 Quote Of course, there's a big difference between using models to analyze how particular aspects of evolution work and trying to model the entire process well enough to say whether it can occur.

Well, that's just being picky!

But yeah, if populate size is allowed to go way up, then the simulation would have to deal with food shortages (i.e., fitness would drop across the board if population gets too large for the food supply).

Henry

Bob O'H

Posts: 1865
Joined: Oct. 2005

Quote (Steve Schaffner @ June 19 2009,13:05)

 Quote You can treat log(si) as you would any standard trait: it's additive, so you can add the genetic and environmental effects.

No it doesn't.    There might be some subtleties in the precise effects, but I wouldn't worry about them.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Bob O'H

Posts: 1865
Joined: Oct. 2005

Quote (Henry J @ June 19 2009,16:16)

 Quote Of course, there's a big difference between using models to analyze how particular aspects of evolution work and trying to model the entire process well enough to say whether it can occur.

Well, that's just being picky! :p

But yeah, if populate size is allowed to go way up, then the simulation would have to deal with food shortages (i.e., fitness would drop across the board if population gets too large for the food supply).

Henry

That's not too difficult to implement.  You scale the fitness so that it is above 1 at low density, and then apply some density dependence to it, which affects all genotypes equally.  Then you draw the number of offspring from a Poisson distribution with that mean.

i.e. if fitness is si, and the population size is N, then the number of offspring is Poisson distributed with mean si exp(-N/K), for example (exp(-N/K) is the discount for density).

I think doing it this way is nicer because (a) it's easier to programme (there's no independence problems, or multinomials with huge N's), and (b) you're modelling absolute fitness, so it can go below 1, and you might get extinctions.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (Steve Schaffner @ June 18 2009,20:53) And the whole thing is pretty convoluted, when the essence of the model could be captured simply by assigning a fitness to the genotype and then calculating the number of offspring.

From much of this discussion, it's easy to see how many different ways there are to abstract an evolutionary process.

These are the primary attributes I've found in Mendel's Accountant:

* Population of Genotypes (genotypic fitness).
* Genotype modified by heritability and noise to Phenotype (phenotypic fitness).
* Genotype further modified for chance of reproductive success to Working Fitness.
* Number of offspring proportional to sqrt(Phenotype).
* Reproduction with mutation.
* Throw in more random factors, such as random death.

> The calculation of Phenotype is not scaled.
> The calculation of Working Fitness is division by Randomnum. Not the sqrt(randomnum), not some other exponent. Or even a normal binomial to determine reproductive success.
> Number of offspring proportional to sqrt(Phenotype). Why the square root? Why not some other exponent?
> Random death? Isn't that already accounted for in phenotypic fitness (for stillbirths) or chance of reproductive success?

You may as well throw in another factor that randomizes falling off a cliff and whether she has a headache, and just make up numbers or exponents or parameters that seem right. The problem is the qualitative nature of the simulation and the arbitrariness of some of the assumptions. I just don't see Mendel's Accountant being salvageable as a quantitative model of biology.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Bob O'H

Posts: 1865
Joined: Oct. 2005

Quote (Zachriel @ June 19 2009,17:34)

 Quote (Steve Schaffner @ June 18 2009,20:53) And the whole thing is pretty convoluted, when the essence of the model could be captured simply by assigning a fitness to the genotype and then calculating the number of offspring.

From much of this discussion, it's easy to see how many different ways there are to abstract an evolutionary process.

These are the primary attributes I've found in Mendel's Accountant:

* Population of Genotypes (genotypic fitness).
* Genotype modified by heritability and noise to Phenotype (phenotypic fitness).
* Genotype further modified for chance of reproductive success to Working Fitness.
* Number of offspring proportional to sqrt(Phenotype).
* Reproduction with mutation.
* Throw in more random factors, such as random death.

No recombination?  We know about mutational meltdown, and we also know that sex can mitigate the effect.

 Quote You may as well throw in another factor that randomizes falling off a cliff and whether she has a headache,

Don't ask Gil to help with the programming for that.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Bob O'H @ June 20 2009,02:14)

Quote (Zachriel @ June 19 2009,17:34)

 Quote (Steve Schaffner @ June 18 2009,20:53) And the whole thing is pretty convoluted, when the essence of the model could be captured simply by assigning a fitness to the genotype and then calculating the number of offspring.

From much of this discussion, it's easy to see how many different ways there are to abstract an evolutionary process.

These are the primary attributes I've found in Mendel's Accountant:

* Population of Genotypes (genotypic fitness).
* Genotype modified by heritability and noise to Phenotype (phenotypic fitness).
* Genotype further modified for chance of reproductive success to Working Fitness.
* Number of offspring proportional to sqrt(Phenotype).
* Reproduction with mutation.
* Throw in more random factors, such as random death.

No recombination?  We know about mutational meltdown, and we also know that sex can mitigate the effect.

Oops. Good point. Haven't got that far in reconstructing the algorithm, but of course. Add it to the list of primary attributes.

c...        Randomly mate one half of the population with members
c...        from the other half.

&            1 + int(current_pop_size*randomnum(1)))

end do

mom = min(current_pop_size,
&            1 + int(current_pop_size*randomnum(1)))
do while(.not.available(mom))
mom = mod(mom, current_pop_size) + 1
end do
available(mom) = .false.

Looks like asexual recombination, i.e. two random individuals.

if(randomnum(1) < 0.5) then
else
parent = mom
end if

This looks like a bit of the actual recombination event:

if(.not. clonal_reproduction) then

<... snip ...>

do ch=1,haploid_chromosome_number

ls0 = (ch - 1)*chr_length + 1
ls1 = min(chr_length-1, int(chr_length*randomnum(1))) + ls0
ls2 = min(chr_length-1, int(chr_length*randomnum(1))) + ls0

<... snip ...>

&      hap_id = min(2, 1 + int(2.*randomnum(1)))

The rest of the code seems a lot more complicated than need be, but that might just be because of the dynamic linkage option. The command do ch=1,haploid_chromosome_number appears twice in the code. (Indentation doesn't seem to be consistent so it's hard to know where the beginning and ending of sections are to be found, though that might be my reader.)

I still think the problem with Mendel's Accountant is more basic, and breaks before that point.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Bob O'H @ June 20 2009,02:14)

 Quote You may as well throw in another factor that randomizes falling off a cliff and whether she has a headache,

Don't ask Gil to help with the programming for that.

Why not? He helped me with Word Mutagenator.

 Quote GilDodgen: This is not hard: Simply print out the dictionary contained in the Zachriel program and be done with it. No search is necessary, because all the requisite information has been supplied by the programmer in advance.

GilDodgen has never quite grasped the concept of a model. (In Word Mutagenation, the evolving sequences have no knowledge of the word-scape. They either survive or die. It was an ID challenge, by the way, not my idea.) They never did let me respond on their blog.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

k.e..

Posts: 2498
Joined: May 2007

ok here goes, if I'm off target correct me gently.

I know very little about the ins and outs of the GA problem space but I do understand modeling as a concept.

It seems to me that the simple weasel GA resultant graph over time IS the fitness landscape.

So to test the fitness landscape several things could be tried and they probably already have been so again I'm just an amature.

A couple of simple additions could be to add a second sentence that competes for only a few common letters of the alphabet that are in the first sentence ...and see what happens.

Only a limited number of letters are available for all species of sentence perhaps based on the usage count in English.

Then add an in vivo preditor which eats the letters from one of the first species but leaves a few letters on the savanah or petrie dish.

Then from the leftovers see if a new species of gramatically correct (dare I say Shakesperean quotation) arises.

Compare the fitness landscapes with known population statistics for a simple analog and reduce errors.

--------------
"I get a strong breeze from my monitor every time k.e. puts on his clownDaveTard suit." dogdidit

Abbie Smith (ERV) who's got to be the most obnoxious arrogant snot I've ever seen except for when I look in a mirror. DAVE TARD

Henry J

Posts: 3735
Joined: Mar. 2005

 Quote whether she has a headache

Is there a variable for the amount of aspirin that's available?

Henry

dvunkannon

Posts: 1359
Joined: June 2008

From the MEDAL (Missouri Estimation of Distribution Algorithms Laboratory blog

 Quote John H. Holland will give a keynote speech at GECCO-2009 on July 12, 2009 (Sunday), 10:40am-11:40am. The talk is entitled Genetic Algorithms: Long Ago [Past] and Far Away [Future] and the abstract of the talk follows:It was in the mid-50’s of the 20th century when I realized that Fisher’s fundamental theorem could be extended from individual alleles to co-adapted sets of alleles, without linearization. That led to a realization that recombination, rather than mutation, was the main mechanism providing grist for the natural selection mill. There was little theory concerning recombination in those days, but now recombination is a standard explanation for biological innovations, such as swine flu.Much later, in the early 1990’s, GA’s provided the “adaptive” part of rule-based models of complex adaptive systems (CAS), such as the artificial stock market pioneered at the Santa Fe Institute. Tag-based signal processing occurs in systems as different as biological cells, language acquisition, and ecosystems. CAS models offer a unified way to study the on-going co-evolution of boundary and tag networks in these systems

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

slpage

Posts: 349
Joined: June 2004

Quote (Steve Schaffner @ June 19 2009,13:33)
 Quote (slpage @ June 19 2009,13:09) I am still wondering why they think that constraining the outcomes to a constant population size is biolgically realistic.

It's a feature of many population genetics models. It has the advantage of being simple. How accurate it is depends a lot on what organism you're looking at.

Of course, there's a big difference between using models to analyze how particular aspects of evolution work and trying to model the entire process well enough to say whether it can occur.

Which is sort of my point.  Their claim is that this is state of the art and realsitic, yet they appear to have employed certain parameters for simplicity rather than realism.

Constant population size is one of my pet peeves with Haldane's model as well.

slpage

Posts: 349
Joined: June 2004

Quote (Henry J @ June 19 2009,16:16)
 Quote Of course, there's a big difference between using models to analyze how particular aspects of evolution work and trying to model the entire process well enough to say whether it can occur.

Well, that's just being picky! :p

But yeah, if populate size is allowed to go way up, then the simulation would have to deal with food shortages (i.e., fitness would drop across the board if population gets too large for the food supply).

Henry

I don't mean just allowing it to grow willy nilly - that is not realistic, either.  But if they want to claim 'most realistic' then it seems to me employing non-universal constraints negates that claim.

I also gather that while deleterious mutations are allowed to accumulate and not reduce, that beneficials are allowed to be lost.  Is that correct?

Steve Schaffner

Posts: 13
Joined: June 2009

 Quote (slpage @ June 23 2009,19:13) I don't mean just allowing it to grow willy nilly - that is not realistic, either.  But if they want to claim 'most realistic' then it seems to me employing non-universal constraints negates that claim.

True, but I doubt variation in population size would have much effect on the long-term fate of the population.
 Quote I also gather that while deleterious mutations are allowed to accumulate and not reduce, that beneficials are allowed to be lost.  Is that correct?

I haven't been running the program, but I haven't seen anything obviously wrong with how they handle beneficial and deleterious mutations. In the real world, deleterious mutations of very small effect really do accumulate, and most beneficial mutations really are lost.

AnsgarSeraph

Posts: 11
Joined: June 2009

I've been in touch with Dr. Wes Brewer, who maintains the MENDEL code. He's still working on the Linux flavors and hopes to have a production copy soon. He's not comfortable posting it on a public site, at the moment, but said that I could privately supply the link to people here who want it.

The "big deal" aspect of it is that this is version 1.4.5. It looks like the source code is packaged in "cgi-bin/cmendel" and "cgi-bin/fmendel" (but that's just from the README . . . I don't know code so I didn't open any files).

Anyone who wants this thing can email me @

whitebriar - gmail - com

—Sam

oldmanintheskydidntdoit

Posts: 4999
Joined: July 2006

 Quote (AnsgarSeraph @ June 24 2009,21:24) I've been in touch with Dr. Wes Brewer, who maintains the MENDEL code. He's still working on the Linux flavors and hopes to have a production copy soon.

Mah, a java version would be better IMHO - install apache etc ????

Then it would work on Linux, Windows, MacOS anything that has a JVM in fact.

--------------
I also mentioned that He'd have to give me a thorough explanation as to *why* I must "eat human babies".
FTK

if there are even critical flaws in Gauger’s work, the evo mat narrative cannot stand
Gordon Mullings

Zachriel

Posts: 2560
Joined: Sep. 2006

The calculation of "working fitness" is seemingly broke. From Mendel's Accountant:

do i=1,total_offspring
work_fitness(i) = work_fitness(i)/(randomnum(1) + 1.d-15)
end do

We can test this by taking a series of fitnesses k from 1.001 to 2,

For k = 1 To 1000
Cells(k, "a") = 1+ k / 1000
Cells(k, "b") = Cells(k, "a") / Rnd
Next k

This is a typical result:

9 Average
31 St.Dev.
362% Relative St.Dev.
1.04 Min
533 Max

The original distribution of k has a Relative St.Dev. of 19%. It's worse for fitnesses distributed between 0.5 and 1.5 or 0.5 and 1. (Just like the phylogenetic fitness, the calculation is not normalized. And why ÷Rnd^1? Why not ÷Rnd^½ or ÷Rnd^¾?) This single operation eliminates the vast majority of the signal from genetic or phylogenetic fitness.

A more reasonable calculation is Roulette Wheel selection.

I have a working version of Gregor's Bookkeeper. I'll post on that in the next few rotations.

-
Fixed a problem.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

Ranking is the actual measure, not the nominal working fitness. If we just look at 1000 trials, comparing the actual rank to the expected rank, a perfect fitness signal will have a St.Dev. of zero; 1,2,3,4,5 ...

Ranking of random fitness has a St.Dev of about 410 from the expected rank. Using (1 + k / 1000)÷Rnd the St.Dev between the expected rank and the actual rank is about 320-350. A very weak signal.

This is sorted by Working Fitness. What was ranked 863 is now first on the list.

Phylo      Working       Phylo
Fitness     Fitness       Rank
1.863   15,077.64      863
1.526     5,482.23      526
1.759       591.73       759
1.298       369.09       298
1.413       274.25       413
1.986       207.86       986
1.781       194.58       781
...

Of course, there are many other sources of noise that dilute the fitness signal, but this is the most egregious departure from model realism.

-
A different run than the previous. They're both typical and should be easy to verify.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (Zachriel @ June 25 2009,09:41) The calculation of "working fitness" is seemingly broke. From Mendel's Accountant:      do i=1,total_offspring           work_fitness(i) = work_fitness(i)/(randomnum(1) + 1.d-15)        end doWe can test this by taking a series of fitnesses k from 1.001 to 2,  For k = 1 To 1000       Cells(k, "a") = 1+ k / 1000       Cells(k, "b") = Cells(k, "a") / Rnd   Next kThis is a typical result: 9 Average 31 St.Dev.362% Relative St.Dev. 1.04 Min 533 MaxThe original distribution of k has a Relative St.Dev. of 19%. It's worse for fitnesses distributed between 0.5 and 1.5 or 0.5 and 1. (Just like the phylogenetic fitness, the calculation is not normalized. And why ÷Rnd^1? Why not ÷Rnd^½ or ÷Rnd^¾?) This single operation eliminates the vast majority of the signal from genetic or phylogenetic fitness. A more reasonable calculation is Roulette Wheel selection.I have a working version of Gregor's Bookkeeper. I'll post on that in the next few rotations.-Fixed a problem.

I assume you mean the scaling of fitness by total population fitness used in routlette selection, correct?

I'm not following the dissection of MA, is work_fitness being used to drive a selection algorithm? If so, the division by rnd() is equivalent to assuming that all selection takes place after a night of drinking heavily.

Roulette selection assumes that details don't matter - of several equally snappily dressed men at the bar, the one with the clean fingernails will not be selected much more frequently than the rest with dirty fingernails. Is this "realistic"? What does MA assume about sexual selection?

A lot of GAs use tournament selection to maintain a more constant selection pressure. I think you could argue that tournament selection models some part of the sexual selection process.

ETA - or give users a choice of selection algorithm.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (dvunkannon @ June 25 2009,10:07) I assume you mean the scaling of fitness by total population fitness used in routlette selection, correct?

Yes, the scaling determines probability of successful reproduction.

 Quote (dvunkannon @ June 25 2009,10:07) I'm not following the dissection of MA, is work_fitness being used to drive a selection algorithm? If so, the division by rnd() is equivalent to assuming that all selection takes place after a night of drinking heavily.

Close, but not quite. Actual reproductive success involves more than mating selection. There is still a strong component of phylogenetic health involved. Reproductively healthier individuals will tend to mate more often and produce more offspring, even when they mate randomly. (Perhaps you should spend more time in bars—for observational purposes, of course.)

 Quote (dvunkannon @ June 25 2009,10:07) Roulette selection assumes that details don't matter - of several equally snappily dressed men at the bar, the one with the clean fingernails will not be selected much more frequently than the rest with dirty fingernails. Is this "realistic"?

Yes. Minor differences tend to have minor effects. There is no amplification for discernment. Like the bar at 10 PM rather than 1 AM (i.e. after only moderate alcohol consumption).

 Quote (dvunkannon @ June 25 2009,10:07) What does MA assume about sexual selection?

It doesn't (as far as I know).

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

Gregor's Bookkeeper is now workable enough to report some results. But first the implementation.

Gregor's Bookkeeper doesn't keep track of all mutations, just their cumulative effect. The only concern at this point is average fitness and genomic decay. But it does allow us to inspect the population to see what is happening. Bells and whistles don't matter much in the face of the claim that Mendel's Accountant has overthrown 150 years of biological science. We eagerly anticipate replicating this exciting discovery.

Each member of the population is comprised of a collection of genes. Mutations can occur randomly in any member and any gene. Most mutations are very nearly neutral.

Beneficial mutations are often dominant because they can cover for the activity of a weaker partner. But sometimes alleles can combine effects, or deleterious mutations can sometimes even be dominant. This aspect of the model is still unsatisfying, so we're leaving Dominant = 50%, Recessive = 50%.

Phylogenetic Fitness is normalized. That means Environmental Noise scales properly with fitness.

PhyloIndex holds the sort of the Phylogenetic Fitness. We take those with the highest Phylogenetic Fitness to enter the mating round (after eliminating those with very low fitness).

Those with higher Phylogenetic Fitness are more likely to mate and produce a large number of offspring. The first step is to create an accumulation of normalized fitnesses. Then we use this table to determine winners and losers. Finally, we select a random allele from each Parent.

Other aspects include allowing normal variations in population, average offspring and mutation rates over time.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3138
Joined: Mar. 2008

I think you left out the smoke and mirror generators and the obfuscationizer.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (midwifetoad @ June 25 2009,21:28) I think you left out the smoke and mirror generators and the obfuscationizer.

Mirrors are easy, but modeling smoke. That's hard.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Henry J

Posts: 3735
Joined: Mar. 2005

 Quote (Zachriel @ June 26 2009,06:31) Mirrors are easy, but modeling smoke. That's hard.

And hazardous to the health.

Zachriel

Posts: 2560
Joined: Sep. 2006

Quote (Zachriel @ June 25 2009,11:00)

 Quote (dvunkannon @ June 25 2009,10:07) {snipped} ... equivalent to assuming that all selection takes place after a night of drinking heavily.

(Perhaps you should spend more time in bars—for observational purposes, of course.)

I trust, dvunkannon, that your field work is progressing well.

 Quote (dvunkannon @ June 25 2009,10:07) A lot of GAs use tournament selection to maintain a more constant selection pressure. I think you could argue that tournament selection models some part of the sexual selection process.

I've been thinking about your analogy and suggestions. Roulette seems to emulate relative fecundity. Those with higher fecundity will tend to mate with those of higher fecundity simply because they mate and produce offspring more often. (Wimps pass out in the corner, rarely mating.) But Tournament does tend to pair those with like-fecundity, so I suppose it does seem like sexual selection. I may try that next. I thought about some more explicit method, but that might be beyond what we are trying to accomplish with this model.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

There was some question about the mutation algorithm. It's based on a mirror of this RndGamma distribution. {The lip at 1.000000 is due to cutting off the maximum effect at an arbitrary level.}

The probability distribution of mutational effect tends to be very small, with larger effects increasingly rare.

The routine usually returns a multiplier of just a little more than one (favorable), or a multiplier of just a little less than one (deleterious).

Deleterious and favorable mutational effects have an identical distribution, except that favorable mutations are generally much rarer. I'm using the Excel Application GammaDist function. It's not a perfect distribution, but should be more than sufficient for our purposes.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

The qualitative results of Gregor's Bookkeeper are fairly straightforward. If the rate of reproduction is low compared to the mutation rate, that is, if many offspring are mutants rather than clones, then deleterious mutations will tend to accumulate in genomes. (They will actually eventually accumulate with any finite population.)

But every once in a while, a significant and favorable mutation will sweep through the population. For a given ratio of favorable to beneficial mutations, the larger the population, the less the fitness will drop before a beneficial mutation has a chance to sweep through the population. So for a given setting, just dial-up the population (or reproductive rate) to avoid genomic meltdown.

Now consider a large population that has been divided into small isolated groups. Many will meltdown. But some, by chance, will experience favorable mutations that will sweep the population. Then if the population is allowed to grow, this subpopulation can avoid genomic meltdown and experience further gains in fitness. Lots of branches, most of which fail, but a few that then prosper. Adaptive radiation.

And we have yet to account for sexual selection, or hybridization.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3138
Joined: Mar. 2008

Forgive me for having trouble with the meltdown concept. Is there an observed instance in the real world of a reproducing population going extinct due to the accumulation of deleterious mutations?

What would that look like?

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

oldmanintheskydidntdoit

Posts: 4999
Joined: July 2006

 Quote (midwifetoad @ July 02 2009,10:11) Forgive me for having trouble with the meltdown concept. Is there an observed instance in the real world of a reproducing population going extinct due to the accumulation of deleterious mutations?What would that look like?

http://www.uncommondescent.com/

ba-dum-tish!

--------------
I also mentioned that He'd have to give me a thorough explanation as to *why* I must "eat human babies".
FTK

if there are even critical flaws in Gauger’s work, the evo mat narrative cannot stand
Gordon Mullings

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (midwifetoad @ July 02 2009,10:11) Forgive me for having trouble with the meltdown concept. Is there an observed instance in the real world of a reproducing population going extinct due to the accumulation of deleterious mutations?What would that look like?

In asexual populations, it's rather easy to understand. If most offspring are mutants, they are nearly always of lower fitness. Organisms such as bacteria have low individual mutation rates, and most offspring are exact clones. Even then, they often recombine genetic material.

It's a bit different in sexually reproducing species such as vertebrates. Bottleneck populations, species that have had their habitat severely reduced, or varieties near range edges, are believed to suffer mutational meltdown. Reproductive rates and numbers of viable offspring tend to decline due to inbreeding, and that can make the species vulnerable to extinction. It's a problem with many species being pushed to the brink by humans.

Hybridization can sometimes reinvigorate a species.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3138
Joined: Mar. 2008

I'm still a bit confused. It is commonly argued, and has been for many decades, that low population numbers lead to extinction, even if the remaining members are protected.

But I fail to get the relevance for TOE. Extinction seems to be a rather common event at geological time scales, so what's the problem for the theory?

I'm thinking the definition of deleterious is rather arbitrary unless it is exposed to selection. What prevents us from defining all niche specializations as deleterious at geological time scales, since specialization puts a population at risk for extinction.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (midwifetoad @ July 02 2009,11:22) I'm still a bit confused. It is commonly argued, and has been for many decades, that low population numbers lead to extinction, even if the remaining members are protected.But I fail to get the relevance for TOE. Extinction seems to be a rather common event at geological time scales, so what's the problem for the theory?

The claim is that genetic meltdown affects even normal-size populations. Of course, Mendel's Accountant ignores many factors that mitigate against the claim.

 Quote (midwifetoad @ July 02 2009,11:22) I'm thinking the definition of deleterious is rather arbitrary unless it is exposed to selection. What prevents us from defining all niche specializations as deleterious at geological time scales, since specialization puts a population at risk for extinction.

You're right in general.

Presumably, Mendel's Accountant is considering absolute fitness, meaning populations can precipitously degrade if the fecundity drops below a certain level. If they were serious, they would investigate under which circumstances they would not see meltdown, and explore this boundary in more detail. But their purpose is apologetics dressed up as science. I certainly wouldn't consider any such simulation to be anything more than qualitatative unless carefully matched to a biological situation. It doesn't even account for sexual selection; and without empirical verification, it is subject to simple mistakes such as the calculation of "working fitness" noted above. They see what they want to see.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Posts: 3138
Joined: Mar. 2008

I'd think if you were trying to model a natural phenomenon, you would have an example in mind. Something from the real world to model. But maybe that's just me.

I haven't heard of any decline in the fecundity of asexual organisms. All the species I know of that are listed as in danger of extinction seem to be in danger due to reduction of or rapid change in habitat,  or due to introduction of diseases or predators. Neither of these causes involves genetic entropy.

I just wonder what observation led to the genetic entropy hypothesis.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Bob O'H

Posts: 1865
Joined: Oct. 2005

 Quote (midwifetoad @ July 02 2009,12:29) I'd think if you were trying to model a natural phenomenon, you would have an example in mind. Something from the real world to model. But maybe that's just me.

Mendel's Accountant is meant to be used to make a general point about evolution, so they don't want  to burden the model with the specifics of one system.

 Quote I haven't heard of any decline in the fecundity of asexual organisms. All the species I know of that are listed as in danger of extinction seem to be in danger due to reduction of or rapid change in habitat,  or due to introduction of diseases or predators. Neither of these causes involves genetic entropy.

Well, yes.  I'm a bit cynical about a lot of conservation genetics: far too many people doing it use species of great conservation concern, like fruit flies.

In think there has been some work showing it happens in nature, but there are so many other threats as well.

 Quote I just wonder what observation led to the genetic entropy hypothesis.

The Fall.

Sanford is a YEC, so his argument is that things have been getting worse since The Fall, and all we can do is pray.

--------------
ID theorists don’t postulate a designer for their arguments. - Crandaddy
There is no connection between a peppered moth, natural selection, and religion that I can see. - FtK

Henry J

Posts: 3735
Joined: Mar. 2005

 Quote (midwifetoad @ July 02 2009,11:29) I just wonder what observation led to the genetic entropy hypothesis.

Observation? Observation? We don't need no steeenkin pathetic-level-of-detail observation!!111!!eleven1!!

dvunkannon

Posts: 1359
Joined: June 2008

Quote (Zachriel @ July 02 2009,10:25)
Quote (Zachriel @ June 25 2009,11:00)

 Quote (dvunkannon @ June 25 2009,10:07) {snipped} ... equivalent to assuming that all selection takes place after a night of drinking heavily.

(Perhaps you should spend more time in bars—for observational purposes, of course.)

I trust, dvunkannon, that your field work is progressing well.

 Quote (dvunkannon @ June 25 2009,10:07) A lot of GAs use tournament selection to maintain a more constant selection pressure. I think you could argue that tournament selection models some part of the sexual selection process.

I've been thinking about your analogy and suggestions. Roulette seems to emulate relative fecundity. Those with higher fecundity will tend to mate with those of higher fecundity simply because they mate and produce offspring more often. (Wimps pass out in the corner, rarely mating.) But Tournament does tend to pair those with like-fecundity, so I suppose it does seem like sexual selection. I may try that next. I thought about some more explicit method, but that might be beyond what we are trying to accomplish with this model.

in re: field work and observation - piss poor at the moment. The result of living in New Jersey while dating a Czech supermodel that lives in Prague.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (slpage @ June 23 2009,19:10) Constant population size is one of my pet peeves with Haldane's model as well.

Experimenting with Gregor's Bookkeeper.

VARIABLE POPULATION and FECUNDITY: Setting the number of Parents and the number of Children such that they vary (e.g. 20% relative standard deviation), they tend to achieve a higher fitness. This is apparently because when the population bottlenecks, it weeds out the weaklings. This is somewhat analogous to variations in climate, such as plenitude followed by drought.

TIME: The typical pattern is to watch the fitness slowly ebb away, but then suddenly spring back. If you quit too soon, you would never see this. As long as the population is large enough to be reasonably stable to avoid extinction for long enough, you will see a sawtooth pattern; a slow slide down in fitness, then a sudden increase as a significant favorable mutation sweeps through the population.

DOMINANCE: Still not happy with this feature. As might be expected, setting the fitter allele to be more dominant leads to greater fitness. This might be considered a cheat though. Setting an arbitrary allele to be dominant, it still often leads to greater fitness. An interesting test was to set dominance on a sliding scale, 1/G for G = 1 to numG (G for gene). This means that for some genes, the dominant gene is deleterious, for others favorable. Interestingly, this also leads to greater fitness.

SHAKING the BOX: It almost seems that anything that adds a bit of complex motion allows those with the highest fitness to rise to the top. Need more data.

DIFFERENCES between Gregor's Bookkeeper and Mendel's Accountant:

* Multiplicative fitness.
* Roulette Wheel mating, rather than the odd "divide by random" method.
* Can handle very large populations and generations—if you're willing to wait.
* Raised limit on the effect of favorable mutations. Adjusted some other settings.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

dvunkannon

Posts: 1359
Joined: June 2008

Thinking about the problem of evolving a code, I just wanted to bounce some ideas off of anyone that wants to answer...

Here is a genome design:
6*26 bits of data to build a phenotype, followed by
64*8 bits of coding table

The coding table works like this. Each entry contains an 8 bit string. Each 8 bit string mapt to a list of affinities to a subset of 32 characters:

127 -> 20% A, 40% F, 40% J

This table of affinities would be salted with entries that guarantee A-Z have some entry with high affinity.

The GA works like any binary coded GA. To create a phenotype, run the data section of the genome through the code table 6 bits at a time. The 6 bits are like the three codons in DNA, mRNA, and tRNA. Taken as an index, they give an 8 bit value. The 8 bit value gives a set of affinities, and you spin the roulette wheel to see which letter you get. That letter is the phenotypic expression of the 6 bits you started with. Continue to loop until you've finished the data section of the genome.

To score a phenotype, compare with the string A .. Z and take the sum of the squared error at each position (ex target D, actual A, error is 3).

I think that if it works, you'll eventually get a population with the code table filled with at least one copy of each high affinity 8 bit string.

Comments? Is choosing 32 useful entries out of 256 too easy or too hard?

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Posts: 3138
Joined: Mar. 2008

My little program seeks to build words through cumulative selection. The scoring algorithm is based on the frequency of letter pairs and triplets occurring in actual words. I've built frequency tables for a number of languages.

Selecting just on the relative frequency of pairs and triplets, it builds 7, 8, 9 and ten letter words in very few generations, involving just a few thousands mutations.

More interesting to me is the fact that it builds long word-like strings that look like words and are perfectly pronounceable, but aren't in the dictionary. Also interesting (to me) is the fact that it often ignores dictionary words in selecting the most fit. But despite being word blind, it builds words.

I don't want to read too much biology into this, but I think it blows away Behe's claim that long strings can't be the result of a selection algorithm that is unaware of a goal or target.

My program doesn't have a target or halting condition and continues to produce unique strings for hundreds of generations. I prevent getting stuck on a high scoring mother by periodically killing off the most fit child. Literally, every fourth generation, the most fit bites it.

I don't know if this simulates anything in nature, but to me it indicates that a rather mindless algorithm can do things beyond the ken of rocket scientists.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Posts: 3138
Joined: Mar. 2008

 Quote This table of affinities would be salted with entries that guarantee A-Z have some entry with high affinity.

I'd be curious to know if your concept of affinities has any similarity to my use of letter pairs.

In my limited experience, the output of a GA is limited only by the fitness scoring algorithm. I tried to build the simplest and stupidest scoring algorithm that could still produce interesting results.

My highest priority was to produce a fitness scorer that couldn't be construed as a target.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

dvunkannon

Posts: 1359
Joined: June 2008

Quote (midwifetoad @ July 06 2009,14:07)
 Quote This table of affinities would be salted with entries that guarantee A-Z have some entry with high affinity.

I'd be curious to know if your concept of affinities has any similarity to my use of letter pairs.

In my limited experience, the output of a GA is limited only by the fitness scoring algorithm. I tried to build the simplest and stupidest scoring algorithm that could still produce interesting results.

My highest priority was to produce a fitness scorer that couldn't be construed as a target.

The affinities are my way to model the situation that tRNA molecules could evolve from molecules that accepted any of several amino acids before getting more specific. Right now there is still one tRNA that will accept two different AAs, but instead of evolving a more specific acceptor, the system fixes up errors after they occur.

We're aiming to do different things, so I don't see a conceptual overlap between your bigram and trigram tables and what I was conjecturing. I think what you are doing is using a Hidden Markov Model to evolve phoneme level utterances. I think Chomsky showed that human language is more than an HMM, but as you said, Behe doesn't think evolution can even do that much!

ETA - I take your point on the scoring. I thought of taking the data portion out of the genome, and just testing it against various strings, but I think going against a fixed target might be a simple first test of the idea.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Posts: 3138
Joined: Mar. 2008

 Quote I think Chomsky showed that human language is more than an HMM, but as you said, Behe doesn't think evolution can even do that much!

I certainly am not ambitious enough to attempt evolving language. My goal was simply to test Behe's edge of evolution in a way that is a notch less deterministic than Dawkin's original Weasel.

I know, for example, that selecting for phenotypes is theoretically different from selecting genotypes, but I don't see how this matters much in a model. If the phenotype doesn't reflect the genotype in a way that is visible to the selecting agent, it doesn't really matter.

As for evolving phoneme level utterances, I think that's quite appropriate for a demo program. For one thing, I can make trade names. Bactine, for example. The distinguishing features of a trade name are pronounceability and novelty.

I don't know much about Chomsky except that he said a lot of things that sound like ID. I resist his pronouncements for that reason alone.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Henry J

Posts: 3735
Joined: Mar. 2005

At a guess, computing a phenotype for each genome might make it easier to allow for things like neutral drift, or making some traits more critical than others without necessarily attaching those traits to certain genes.

Henry

Posts: 3138
Joined: Mar. 2008

 Quote (Henry J @ July 06 2009,15:25) At a guess, computing a phenotype for each genome might make it easier to allow for things like neutral drift, or making some traits more critical than others without necessarily attaching those traits to certain genes.Henry

That sounds like saying genes modify the effects of other genes.

But unless you have some way of modeling biological development, you are not being more "realistic" than my database of letter combinations known to have fitness.

I can quickly assign a fitness score to any arbitrary string of characters, comparing say EUPOUACCT to EUPOUAGCT, and choose one to be the parent of the next generation.

Now if you were doing a straight Weasel program you could just count the number of letters that match your target. But I am not searching for a target. I am shaping a population to look and sound like words from a specific language.

I'm not claiming to model biology, and I have doubts that we have the ability at this time to model biology. What we can do is model specific claims and specific assumptions.

All I set out to do was respond to criticisms of Dawkins asserting that his program did nothing but seek a fixed target. The demonstrable fact is that a clever selector can build unanticipated structures -- functional strings much longer than those known to the selector.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

dvunkannon

Posts: 1359
Joined: June 2008

 Quote (midwifetoad @ July 06 2009,17:05) Now if you were doing a straight Weasel program you could just count the number of letters that match your target. But I am not searching for a target. I am shaping a population to look and sound like words from a specific language.

Yes, your system would stump the level 0 critic because you don't have a target hard coded that looks exactly like a population member.

The level 1 critic would say that the bigram and trigram table is hard coded, and that your system is rewarding population members that have the same frequency distributions as the table. That is your target.

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Posts: 3138
Joined: Mar. 2008

Just as soon as cdesign proponentsists come up with a level one critic, I'll worry.

But the general argument is that selection doesn't work. Or that it can't make anything new.

The demonstrable fact is that a selector that works at the organism level allows novelty to arise. If you toss in an occasional asteroid, it continues to arise over many generations . That takes care of latching and virtual latching.

Regardless of how you parse the argument, my dumbass selector makes words that could be in the dictionary, but aren't. It also makes things like jargon and acronyms that are in common use but aren't allowed in my dictionary (developed for Scrabble).

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Posts: 3138
Joined: Mar. 2008

Selection has to be, at some level, hard coded.

At the biochemical level, most combinations are non-viable. (More ways to be dead than alive.)

At the population level, the selecting environment is pretty discriminating. diseases and predators are a lot smarter and more versatile than my probability tables.

I use tables for two reasons. One is that they are easy, fast and practical. The second is that they reflect a history of language evolution. Unless you don't believe language evolves.

They are a shorthand for all the historical events that made some strings of letters words and some not.

I am certain that some of the children produced by my demo that are not currently in use will be. One of the cute words that popped up was "mindfly." Not surprisingly, there is a mindfly.com.

So I think my selector does, at some elementary level, model one of the ways language evolves.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Henry J

Posts: 3735
Joined: Mar. 2005

 Quote Selection has to be, at some level, hard coded.

Yeah, it seems likely (to me at least) that some aspects would have to be directly programmed.

One way to get at least partly around that (at the cost of a bunch more work) is multiple species interacting with each other - in that case, the "target" is a balance between tasting bad to the other guys, and being able to catch (and digest) enough of the other guys to get by. That might be one way to keep it from converging on any one final answer.

Henry

Posts: 3138
Joined: Mar. 2008

The aspect of my simple weasel program that I find most interesting is the period after selection creates a population having high fitness. It then dithers around -- sometimes for fifty or a hundred generations -- without increasing fitness. Then, suddenly a long word will pop out, as if the program were waiting patiently for some hopeful monster.

My reading is that the plateau of high fitness is where any living population resides, and that so called neutral mutations are not really neutral, except that they have a fitness level as high as the existing average. Or perhaps they balance -- one up, one down.

It isn't really necessary to invoke any miracles of improbability. The dithering at high levels of fitness allows all kinds of things to emerge which could not emerge in one step from a low level of fitness.

I record the fitness scores of the mothers in my demo. When I see a 10 letter word emerge out of nowhere, I can trace the mothers and their fitness scores. Doing this, I see that for the most part, the history exhibits the same kind stepwise shift toward the 10 letter word that Dawkins' Weasel shows, as if that particular word were the goal.

Because I kill off the fittest every now and then, the scores sometimes go backwards. Sometimes the total count of "correct" letters will go backwards. All the more astonishing in retrospect. Dawkins' program can't do that.

But of course I wrote the program and I know the stepwise movement toward a target is an illusion. The program merely insures that given an adequate level of fecundity, fitness will always be high. After the first twenty or thirty generations, the population is never more than a few steps away from a "breakthrough."

I think what the cdesign proponentsists are missing is the fact that stasis in level of fitness does not imply genetic stasis. There are many variations that are equivalent in fitness. The path to a breakthrough structure doesn't have to  involve a continuous increase in overall fitness.

As an author, I'm biased, but I think my little program allows a person to see genetic drift. You see every mutation and every child. Of course I could be completely wacked.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

dvunkannon

Posts: 1359
Joined: June 2008

 Quote Sometimes the total count of "correct" letters will go backwards. All the more astonishing in retrospect. Dawkins' program can't do that.

Errr, no. That was what the whole latching kerfuffle was about!

--------------
I’m referring to evolution, not changes in allele frequencies. - Cornelius Hunter
I’m not an evolutionist, I’m a change in allele frequentist! - Nakashima

Henry J

Posts: 3735
Joined: Mar. 2005

Yeah, they couldn't seem to get that offspring with a backwards mutation would be less fit, and so wouldn't be selected, and so wouldn't show up in the next generation. Direct logical consequence of the algorithm, no special coding needed.

Henry

Posts: 3138
Joined: Mar. 2008

Quote (dvunkannon @ July 07 2009,12:16)
 Quote Sometimes the total count of "correct" letters will go backwards. All the more astonishing in retrospect. Dawkins' program can't do that.

Errr, no. That was what the whole latching kerfuffle was about!

The latching crowd asserted that once a letter was correct it was protected from mutating. That's a separate issue from whether the total number of correct letters can decline.

At reasonable mutation rates, there will "always" be at least one perfect, unmutated child in the Dawkins program, and the total fitness will never decline. It is theoretically possible for two good mutations to offset one bad one, so it is possible for a good letter to revert.

My program pretty much guarantees that every fourth generation will decline in total fitness.

Since my fitness definition is much broader than a single target, I stir the pot to avoid getting stuck on a single individual having a high fitness score.

I don't know if I am modelling anything real. My only concern is to demonstrate that selection can do interesting things without a specific target.

I would argue, however, that my fitness database is at least partially equivalent to a real selection history. Which is to say, the distribution of letter pairs or phonemes in a real language is the result of a real selection history, and the database embodies that history.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Posts: 3138
Joined: Mar. 2008

Here's what I'm thinking, right or wrong:

The Behe challenge is to evolve a flagellum from an organism that may have some parts of the structure, but no motor.

Behe and Dembski are thinking that each step must be selected, that is each step must improve fitness; otherwise the probability of building a novel, complex structure is nil.

My argument, based on my own observations, is that when a population is at a high level of fitness -- and any living populations is fit by definition -- there are many variations that neither improve nor degrade fitness. (obviously this isn't an original thought).

This means there is little cost to variation. You may or may not hit upon some new invention, but the cost of "exploring the search space" is nil.

Now I could be just another crank, but I think my program shows this happening in a reasonable time frame. It's a toy illustration, but I think it's worth looking at as a possible teaching device.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Henry J

Posts: 3735
Joined: Mar. 2005

And of course the challenge back to Behe is to explain how his ideas explain a single nested hierarchy being followed by the vast majority of major traits and DNA sequences. (If that isn't being picky.)

Henry

Posts: 3138
Joined: Mar. 2008

 Quote (Henry J @ July 07 2009,21:58) And of course the challenge back to Behe is to explain how his ideas explain a single nested hierarchy being followed by the vast majority of major traits and DNA sequences. (If that isn't being picky.)Henry

I haven't read Edge of Evolution, but I understand he lists many evidences for evolution without mentioning ERVs.

Can't imagine why.

--------------
... a poster child for irresponsible and deceitful misrepresentation of design theory on the Internet.
http://tinyurl.com/9axtwbe....9axtwbe

Zachriel

Posts: 2560
Joined: Sep. 2006

 Quote (Bob O'H @ July 02 2009,13:52) I'm a bit cynical about a lot of conservation genetics: far too many people doing it use species of great conservation concern, like fruit flies.

Heh. Then I suppose you are equally cavalier about the fate of isogenic yeast.

 Quote (Bob O'H @ July 02 2009,13:52) In think there has been some work showing it happens in nature, but there are so many other threats as well.

When populations fall to very low levels, then minor catastrophes can result in extinction. With Gregor's Bookkeeper, if we set the relative variance to a high level (to represent contingent variations), then it can easily result in extinction. A low reproductive rate also makes a species less robust when populations are at a low level or subject to broad environmental fluctuations. Also important in very small populations is the role of recessive genes due to inbreeding. But in nature, we often expect some hybridization with closely related populations.

Some results of Gregor's Bookkeeper. Here are the settings for this trial.

numP is number of Parents.
numT is the number of Turns (but was stopped).
numO is the average number of Offspring.
numG is the number of Genes.
Dominant of 50% has no effect.

(The limiting factor for the software seems to be the Roulette Selection algorithm. The above took a few hours. I may try to optimize it later or convert it to a C++ module—but I doubt it is worth the trouble.)

Even with the very low favorable-to-deleterious ratio of 0.00001, the result was an increase in fitness as seen here.

You can't see it well, but the fitness drops rapidly at the beginning. It's easier to see in log10.

This is because the original population has a uniform (isogenic) fitness of 1, and it takes some time for it to stabilize as a population of nearly neutral mutations.

With a population of 100, it increases in fitness with a favorable-to-deleterious ratio of 0.001 0.002. With a population of 1000, it increases in fitness with a favorable-to-deleterious ratio of 0.0001 0.0002. In nature, a species of only a few thousand individuals is usually considered small.

One of the problems with the model is that {in nature} the ratio of favorable-to-deleterious tends to increase as fitness declines. But leaving that aside ... We see that with larger populations, and within the assumptions of the model, genetic meltdown is not an issue.

The "divide by random" selection algorithm in Mendel's Accountant is flawed. If we add a slight dominance to beneficial alleles or introduce sexual selection, the results would be even more lopsided. The conclusion that genetic meltdown is a problem for reasonably large populations, and that this sort of model indicates a problem for evolutionary theory, is not supported.

-
More accurate value reflects additional data.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006

This gives an idea of the distribution of fitnesses within a population. (The red horizontal line is the mean.) It's fairly uniform except at the margins, but a lot happens at the upper margin. Notice how bottlenecks, though risking extinction, increase overall fitness by weeding out the weakest.

100 numP, Beginning population (of Parents)
100 topP, Population capacity (of Parents)

6 numO, Average Offspring per Parent
20 numG, Genes
0.002 rateFav, Rate of Favorable mutations

20% varChildren, Drift in average fecundity (relative standard deviation)
20% varParents, Drift in average population (relative standard deviation)
5% varMuts, Drift in average mutation rate (relative standard deviation)

Mutational meltdown is only a plausible problem if a population is kept few in number and genetically isolated for a long period of time. Otherwise, bottlenecks can be evolutionary opportunities. The claim that even large populations suffer genetic meltdown, and therefore the world can only be a few thousand years old, is not supported by this sort of modeling.

-
Edit: Fixed graphic so it doesn't show the truncation after previously having calculated the average.

--------------
The struggle against ignorance is to the end of time. But it is said that if you die in tard, you will be reborn in Tardhalla.

Zachriel

Posts: 2560
Joined: Sep. 2006