Information request re: inclusion of ANS models as EA's

From: Wesley R. Elsberry (welsberr@inia.cls.org)
Date: Sat Oct 21 2000 - 13:00:57 EDT

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    [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,
    <http://www.baylor.edu/~William_Dembski/docs_articles/meta139.htm>]

    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

    <http://inia.cls.org/~welsberr/ae/dembski_wa.html#correspondence>



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