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)


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.


Genetic Algorithm Premature Convergence Conservative Behavior Standard Genetic Algorithm Superior Fitness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 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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