A new peer-reviewed paper continues the work published by William Dembski, Robert Marks, and others affiliated with the Evolutionary Informatics Lab. (Check out their new revamped website at EvoInfo.org.) The authors argue that Richard Dawkins’ “METHINKSITISLIKEAWEASEL” evolutionary algorithm starts off with large amounts of active information–information intelligently inserted by the programmer to aid the search. This paper covers all of the known claims of operation of the WEASEL algorithm and shows that in all cases, active information is used. Dawkins’ algorithm can best be understood as using a “Hamming Oracle” as follows: “When a sequence of letters is presented to a Hamming oracle, the oracle responds with the Hamming distance equal to the number of letter mismatches in the sequence.” The authors find that this form of a search is very efficient at finding its target — but that is only because it is preprogrammed with large amounts of active information needed to quickly find the target. This preprogrammed active information makes it far removed from a true Darwinian evolutionary search algorithm. An online toolkit of programs called “Weasel Ware” accompanies the paper and can be found here.
The paper’s title, citation information, and abstract are as follows:
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–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.