By Whitley D.
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Additional resources for A genetic algorithm tutorial
Foundations of Genetic Algorithms, G. Rawlins, ed. Morgan-Kaufmann. pp 69-93. Gorges-Schleuter, M. (1991) Explicit Parallelism of Genetic Algorithms through Population Structures. Parallel Problem Solving from Nature, Springer Verlag, pp 150-159. J. (1986) Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. Systems, Man, and Cybernetics, 16(1): 122-128. J. and Baker, J. (1989) How Genetic Algorithms Work: A Critical Look at Implicit Parallelism. Proc 3rd International Conf on Genetic Algorithms, Morgan-Kaufmann.
Processor) seek a mate close to home. Each processor can pick the best string in its local neighborhood to mate with, or alternatively, some form of local probabilistic selection could be used. 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.
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). Davis argues that hybridizing genetic algorithms with the most successful optimization methods for particular problems gives one the best of both worlds: correctly implemented, these algorithms should do no worst than the (usually more traditional) method with which the hybridizing is done. Of course, it also introduces the additional computational overhead of a population based search.
A genetic algorithm tutorial by Whitley D.