Playing Games with Genetic Algorithms

  • Robert E. Marks
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 100)


In 1987 the first published research appeared which used the Genetic Algorithm as a means of seeking better strategies in playing the repeated Prisoner’s Dilemma. Since then the application of Genetic Algorithms to game-theoretical models has been used in many ways. To seek better strategies in historical oligopolistic interactions, to model economic learning, and to explore the support of cooperation in repeated interactions. This brief survey summarises related work and publications over the past thirteen years. It includes discussions of the use of game-playing automata, co-evolution of strategies, adaptive learning, a comparison of evolutionary game theory and the Genetic Algorithm, the incorporation of historical data into evolutionary simulations, and the problems of economic simulations using real-world data.


Genetic Algorithm Nash Equilibrium Finite Automaton Repeated Game Replicator Dynamic 
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 2002

Authors and Affiliations

  • Robert E. Marks
    • 1
  1. 1.Australian Graduate School of ManagementUniversities of Sydney and New South WalesSydneyAustralia

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