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New Peer-Reviewed ID Paper — Deconstructing the Dawkins WEASEL

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Winston Ewert, George Montañez, William A. Dembski, Robert J. Marks II, “Efficient Per Query Information Extraction from a Hamming Oracle,” Proceedings of the the 42nd Meeting of the Southeastern Symposium on System Theory, IEEE, University of Texas at Tyler, March 7-9, 2010, pp.290-297.

Abstract: Abstract—Computer search often uses an oracle to determine the value of a proposed problem solution. Information is extracted from the oracle using repeated queries. Crafting a search algorithm to most efficiently extract this information is the job of the programmer. In many instances this is done using the programmer’s experience and knowledge of the problem being solved. For the Hamming oracle, we have the ability to assess the performance of various search algorithms using the currency of query count. Of the search procedures considered, blind search performs the worst. We show that evolutionary algorithms, although better than blind search, are a relatively inefficient method of information extraction. An algorithm methodically establishing and tracking the frequency of occurrence of alphabet characters performs even better. We also show that a search for the search for an optimal tree search, as suggested by our previous work, becomes computationally intensive.

[ IEEE | pdf ]

Comments
Congratulations!scordova
March 9, 2010
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In comparison, evolutionary search as modeled by Markov processes uses the Hamming oracle inefficiently.
If you could come up with a mathematical analysis of a search algorithm that took into account a changing target, it would be a much better model of what evolution does. Evolution is the guy who walks through a hardware store and says, "this will work" as opposed to the engineer in a lab who says, "nothing else is acceptable. [The question of moveable targets is an interesting one, but moveable targets don't seem able to bear the weight that evolutionists want to place on them. Organisms, as they evolve and interact with their environment, may alter what counts as fit, but certain fundamental structures are presupposed by life, and these, it seems, constitute targets that are not moveable, such as the protein synthesis apparatus presupposed by all living forms. The denial that evolution constitutes a search thus seems strange and indeed insupportable. Do a search on the phrase "evolutionary search," and you'll find many, many hits. Dawkins' METHINKS IT IS LIKE A WEASEL program, insofar as it is interesting at all, is interesting because it generates a meaningful (to us) string of characters and thus seems to be the conclusion of a search. Yes, the program can be adapted to converge on any other string of 28 characters, but for Dawkins to explain how a computer program that mimics Darwinian evolution could produce a purely random string is hardly interesting. It's that Dawkins' algorithm is successfully executing a search for a salient pattern that makes it interesting. Evolutionists might say that the mere reference to "search" suggests a teleology that is properly absent from evolution. ID theorists would say that that's begging the question, but even if evolution is presumed to be non-teleological, it still requires explanation why evolution is locating places in biological configuration space which in any other context we would regard as the outcome of a teleological process (i.e., search). Evolution, if you will, gives the appearance of "search," and that itself requires explanation. --WmAD]Toronto
March 9, 2010
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From the paper:
We consider searches that are asymptotically perfect, that is, will eventually succeed.
Evolution does not do a search with the intent of finding any specific target at all and neither does the Dawkins Weasel algorithm. You could at any point, in a program based on the Weasel algorithm, replace the target string and the algorithm would home in on the new string. The analogy is like that of shooting at a static target, where you aim directly at the object, compared to shooting at a moving one, where you must lead it.
We are searching for a target message of length L. In general, an (L+1)×(L+1) Markov matrix, P, has elements (P)k,? = pk,? (4) where pk,? is the probability of going to state k given we are in state ?.
I've quoted this because some people on the ID side tend to state the probability of an event as the odds of getting from (state_0) to (state_2^target). Evolution should be mathematically expressed more like the above quote.Toronto
March 9, 2010
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