Parallel Problem Solving from Nature — PPSN III

Volume 866 of the series Lecture Notes in Computer Science pp 16-25


Control of parallel population dynamics by social-like behavior of GA-individuals

  • Dirk C. MattfeldAffiliated withLRW Computing Center
  • , Herbert KopferAffiliated withDepartment of Economics, University of Bremen
  • , Christian BierwirthAffiliated withDepartment of Economics, University of Bremen

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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.