Abstract
A frequently observed difficulty in the application of genetic algorithms to the domain of optimization arises from premature convergence. In order to preserve genotype diversity we develop a new model of auto-adaptive behavior for individuals. In this model a population member is an active individual that assumes social-like behavior patterns. Different individuals living in the same population can assume different patterns. By moving in a hierarchy of “social states” individuals change their behavior. Changes of social state are controlled by arguments of plausibility. These arguments are implemented as a rule set for a massively-parallel genetic algorithm. Computational experiments on 12 large-scale job shop benchmark problems show that the results of the new approach dominate the ordinary genetic algorithm significantly.
Supported by the Deutsche Forschungsgemeinschaft (Project Parnet)
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© 1994 Springer-Verlag Berlin Heidelberg
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Mattfeld, D.C., Kopfer, H., Bierwirth, C. (1994). Control of parallel population dynamics by social-like behavior of GA-individuals. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_246
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DOI: https://doi.org/10.1007/3-540-58484-6_246
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