Cheating for problem solving: a genetic algorithm with social interactions

Rafael Lahoz-Beltra, Gabriela Ochoa, Uwe Aickelin

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Abstract

We propose a variation of the standard genetic algorithm that incorporates social interaction between the individuals in the population. Our goal is to understand the evolutionary role of social systems and its possible application as a non-genetic new step in evolutionary algorithms. In biological populations, i.e. animals, even human beings and microorganisms, social interactions often affect the fitness of individuals. It is conceivable that the perturbation of the fitness via social interactions is an evolutionary strategy to avoid trapping into local optimum, thus avoiding a fast convergence of the population. We model the social interactions according to Game Theory. The population is, therefore, composed by cooperator and defector individuals whose interactions produce payoffs according to well known game models (prisoner's dilemma, chicken game, and others). Our results on Knapsack problems show, for some game models, a significant performance improvement as compared to a standard genetic algorithm.
Original languageEnglish
Pages811-818
Publication statusPublished - 1 Jan 2009
EventGECCO '09: Proceedings of the Genetic and Evolutionary Computation Conference - Montreal, Canada
Duration: 8 Jul 200912 Jul 2009

Conference

ConferenceGECCO '09: Proceedings of the Genetic and Evolutionary Computation Conference
Period8/07/0912/07/09

Keywords

  • Genetic algorithms, social interaction, game theory, knapsack problem

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