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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.

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. This kind of separation is referred to as isolation by distance (Wright, 1932 Muhlenbein, 1991 Gorges-Schleuter, 1991). Of course, neighbors that are only 4 or 5 moves away have a greater potential for interaction. After the rst random population is evaluated, the pattern of strings over the set of processors should also be random.

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. J.

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A genetic algorithm tutorial by Whitley D.


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