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With such small population sizes, however, the population converges to the point that it begins to more or less reproduce many of the same strings. At this point the CHC algorithm uses cataclysmic mutation. All strings undergo heavy mutation, except that the best string is preserved intact. After mutation, genetic search is restarted using only crossover. 3 Hybrid Algorithms L. \Dave" Davis states in the Handbook of Genetic Algorithms, \Traditional genetic algorithms, although robust, are generally not the most successful optimization algorithm on any particular domain" (1991:59).

In either case, only one o spring is produced. and becomes the new resident at that processor. Several people have proposed this type of computational model (Manderick and Spiessens, 1989 Collins and Je erson, 1991 Hillis, 1990 Davidor, 1991). The common theme in cellular genetic algorithms is that selection and mating are typically restricted to a local neighborhood. There are no explicit islands in the model, but there is the potential for similar e ects. Assuming that mating is restricted to adjacent processors, if one neighborhood of strings is 20 or 25 moves away from another neighborhood of strings, these neighborhoods are just as isolated as two subpopulations on separate islands.

1988) GENITOR: a Di erent Genetic Algorithm. Proceedings of the Rocky Mountain Conference on Arti cial Intelligence, Denver, CO. pp 118-130. Whitley, D. and Starkweather, T. (1990) Genitor II: a Distributed Genetic Algorithm. Journal Expt. Theor. Artif. , and Crabb, C. (1992) Tracking Primary Hyperplane Competitors During Genetic Search. Annals of Mathematics and Arti cial Intelligence. 6:367-388. Winston, P. (1992) Arti cial Intelligence, Third Edition. Addison-Wesley. Wright, A. (1991) Genetic Algorithms for Real Parameter Optimization.

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