In many of his publications, including No Free Lunch, Dembski repeatedly discusses Richard Dawkins's METHINKS IT IS LIKE A WEASEL evolutionary algorithm, trying to prove its fallaciousness. This is how Dawkins (1986) describes the weasel algorithm: "It. . . begins by choosing a random sequence of 28 letters It now 'breeds from' this random phrase. It duplicates it repeatedly, but with a certain chance of a random error—'mutation'—in the copying. The computer examines the mutant nonsense phrase, the 'progeny' of the original phrase, and chooses the one which, however slightly, most resembles the target phrase" (47-48).
Dembski (2002b) sees an inadequacy in Dawkins's algorithm: it converges on a target phrase. He says, "choosing a prespecified target sequence as Dawkins does here is deeply teleological. . . . This is a problem because evolutionary algorithms are supposed to be capable of solving complex problems without invoking teleology" (182). But later he says, "An evolutionary algorithm is supposed to find a target within phase space" (203). Searching for a target is teleological.
Such inconsistency is Dembski's trademark. In any case, neither of his statements is correct. Evolutionary algorithms may be either targeted or targetless. Biological evolution, however, has no long-term target. Evolution is not directed toward any specific organism. The evolution of a species may continue indefinitely so long as the environment exerts selection pressure on that species. If a population does not show long-term change, it is not because that population has reached a target but because the environment, which coevolves with the species, acquires properties that eliminate its evolutionary pressure on the species. Dawkins's weasel algorithm, on the other hand, stops when the target phrase has been reached.
Dawkins (1986, 50) was himself the first to point out that his algorithm differs from biological evolution in that it proceeds toward a target. But then a model is not supposed to be a replica of the entire modeled object or phenomenon (Perakh 2002d); models replicate only those features of the modeled objects that are crucial for analyzing a specific, usually limited, aspect of the modeled object or phenomenon and ignore all the aspects and properties that are of minor importance. Dawkins's algorithm was designed to show that a combination of random variations with a suitable law can accelerate evolution by many orders of magnitude; the law in this case is selection. The algorithm indeed shows such an acceleration. As Dembski points out, a random search would require, on average, 1040 iterations of the search procedure. Dawkins's algorithm performs the task in about only forty iterations.
Dawkins's procedure is not a proof of evolution, but it is a valid demonstration of a very significant acceleration of evolution if a suitable law works along with random variations. That is why, as Dembski (2002b) laments, "Dar winists and even some non-Darwinists are quite taken" with Dawkins's example (183). It is, indeed, a good example.
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