Evolutionary Algorithms Based on Game Theory and Cellular Automata with Coalitions

  • Bernabé Dorronsoro
  • Juan C. Burguillo
  • Ana Peleteiro
  • Pascal Bouvry
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)

Abstract

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. However, the use of decentralized populations supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. Hence, in this work we propose a new adaptive technique based in Cellular Automata, Game Theory and Coalitions that allow to manage dynamic neighborhoods. As a result, the new adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed neighborhood for the selected benchmark of combinatorial optimization problems.

Keywords

Game Theory Evolutionary Algorithm Multiagent System Coalition Formation Coalition Structure 
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 2013

Authors and Affiliations

  • Bernabé Dorronsoro
    • 1
  • Juan C. Burguillo
    • 2
  • Ana Peleteiro
    • 2
  • Pascal Bouvry
    • 3
  1. 1.Interd. Centre for Security, Reliability and TrustUniversité du LuxembourgLuxembourg CityLuxembourg
  2. 2.ETSET. Universidad de VigoVigoSpain
  3. 3.Université du LuxembourgLuxembourg CityLuxembourg

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