No Free Lunch (NFL) Lecture

From: Lawrence Johnston (
Date: Mon Jul 15 2002 - 14:33:07 EDT

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    Hi, ASA Listees -

    Here is a new topic to work on, at least I havenít seen it discussed
    in this forum.

    A few weeks ago I attended the weekly seminar of the Bioinformatics
    group, an initiative
    of Computer Science and Biology on our campus. The speaker was
    Darrel Whitely, a
    mathematician of Colorado State.

    His topic was ěNo Free Lunch Theorems in Genetic Computationî.
    This of course immediately reminded me of Bill Dembskiís recent
    book, ěNo Free Lunchî.
       (abbreviated NFL)

    I expected to see a vitriolic denunciation of Dembski and his book,
    but he didnít mention
    Dembski. He went through the process of
    Showing that in general for each type of genetic algorithmic
    computation one can make,
    there is a corresponding NFL theorem. I ended up not being sure
    whether he knew
    Dembskiís book existed.
    His lecture showed that any special search algorithm that is used to
    optimize a given
    quality (such as a fitness function) can on the average be no more
    efficient than a
    RANDOM search. And a random search through all of genome space for
    an animal gets you
    nowhere, for a reasonable population size, and in a time of 10^14
    years. So the NFL
    principle implies that one should not expect to find Darwinnian
    processes that add
    appreciable useful information to a species genome.

    Whitely did not spell out this last inference for the audience, but
    he did not need to,
    for most of them. He was not lynched, he was an honored guest.

    For those who are not into mathematical analysis of genetic
    evolutionary processes, and
    NFL theorems, let me explain a bit.

    Genetic algorithms are computer programs that model the standard
    Neodarwinnian physical
    process of Evolution, which assumes that the increasing adaptedness
    of animals is due to
    random mutations within a population, followed by Natural selection
    of better adapted
    The No Free Lunch conclusion says that one should not, averaged over
    time, and over
    all genetic algorithms, expect purely natural physical processes to contribute
    appreciable information content to an animalís genome.

    This is basically what Dembski says in his ěNFLî book.

    Blessings, Larry Johnston

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