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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.
Third, in general hill-climbing may nd a small number of signi cant improvements, but may not dramatically change the o spring. In this case, the e ects on schemata and hyperplane sampling may be minimal. 9 Hill-climbers or Hyperplane Samplers? In a recent paper entitled, \How genetic algorithms really work: I. Mutation and Hillclimbing," Muhlenbein shows that an Evolution Strategy algorithm using only mutation works quite well on a relatively simple test suite. " (1992:24). This raises a very interesting issue.
1987) Simple Genetic Algorithms and the Minimal, Deceptive Problem. In, Genetic Algorithms and Simulated Annealing, L. , Pitman. Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley. Goldberg, D. (1990) A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing. TCGA 90003, Engineering Mechanics, Univ. Alabama. Goldberg, D. (1991) The Theory of Virtual Alphabets. Parallel Problem Solving from Nature, Springer Verlag.