From welsberr Sat Oct 21 12:00:57 2000 Received: (from root@localhost) by inia.cls.org (8.11.0/8.11.0) id e9LH0vf24879; Sat, 21 Oct 2000 12:00:57 -0500 (CDT) Date: Sat, 21 Oct 2000 12:00:57 -0500 (CDT) From: "Wesley R. Elsberry" Message-Id: <200010211700.e9LH0vf24879@inia.cls.org> To: William_Dembski@baylor.edu Subject: Information request re: inclusion of ANS models as EA's Cc: evolution@calvin.edu, welsberr [Quote] How does the scientific community explain specified complexity? Usually via an evolutionary algorithm. By an evolutionary algorithm I mean any algorithm that generates contingency via some chance process and then sifts the so-generated contingency via some law-like process. The Darwinian mutation-selection mechanism, neural nets, and genetic algorithms all fall within this broad definition of evolutionary algorithms. Now the problem with invoking evolutionary algorithms to explain specified complexity at the origin of life is absence of any identifiable evolutionary algorithm that might account for it. Once life has started and self-replication has begun, the Darwinian mechanism is usually invoked to explain the specified complexity of living things. [End Quote - WA Dembski, Explaining Specified Complexity, ] I would like to see the justification for including "neural nets" in "evolutionary algorithms". It is obvious that the description given in the above quote is inadequate, since many if not most artificial neural system models either do not utilize "chance" or have no necessary dependence upon "chance" processes used as conventions. Given that not all ANS models "sift contingency", how is it accurate to state that the whole field of ANS can be considered a variant of "evolutionary algorithms"? Wesley cc: Calvin evolution reflector, evolution@calvin.edu