Cheating for problem solving: A genetic algorithm with social interactions

Citation

Lahoz-Beltra R, Ochoa G & Aickelin U (2009) Cheating for problem solving: A genetic algorithm with social interactions. In: Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009. GECCO '09 - 11th Annual conference on Genetic and evolutionary computation, Montreal, Canada, 08.07.2009-12.07.2009. New York, NY, USA: ACM, pp. 811-817. http://dl.acm.org/citation.cfm?id=1570013; https://doi.org/10.1145/1569901.1570013

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.