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  Topic: Evolutionary Computation, Stuff that drives AEs nuts< Next Oldest | Next Newest >  
Wesley R. Elsberry



Posts: 4937
Joined: May 2002

(Permalink) Posted: Feb. 10 2017,18:30   

Quote (WebHopper @ Feb. 10 2017,11:32)
Hello,

I am wondering if there is an analytical solution to the Weasel algorithm. I think of a probability distribution of the number of trials necessary to achieve a target sequence, the mean and variance implying the following parameters:

length of the alphabet
length of the target sequence
mutation rate
population size

Have you seen a site where this is done? On evoinfo.org Dembski proposes a solution for what he calls "partitioned search". But I am looking for a solution of what he names "proximity reward search", which apparently is the Weasel algorithm. To be clear, I am not interested in a numerical  but analytical solution of the mean and variance as a functions of the above-mentioned parameters.

Further up the thread, I pointed out the difference between partitioned search and "weasel".


http://www.antievolution.org/cgi-bin....y140986

     
Quote


"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.  



If you have the analytical form you like for partitioned search, then modify to add in the additional element I note for "weasel" and any other adjustments. I left off that project before fully working up the population component for probabilities.

I've had some issues with hosted images going stale. I need to look up some of my graphs in this thread and restore them.

http://austringer.net/wp....-....-part-1

http://austringer.net/wp....-....-part-2

And somewhere, sometime, I know I did a numerical scan of parameter space to show the likely range of parameters for Dawkins' original runs given his reported generation times for results. I'm not finding where I shared that, though.

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

    
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