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

  • Dirk C. Mattfeld
  • Herbert Kopfer
  • Christian Bierwirth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

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.

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Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • Dirk C. Mattfeld
    • 1
  • Herbert Kopfer
    • 2
  • Christian Bierwirth
    • 2
  1. 1.LRW Computing CenterBremenGermany
  2. 2.Department of EconomicsUniversity of BremenGermany

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