Control of Parallel Population Dynamics by Social-Like Behavior of GA-Individuals
نویسندگان
چکیده
A frequently observed diiculty 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. Diierent individuals living in the same population can assume diier-ent 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 signiicantly.
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