A Genetic Algorithm for the Multidimensional Knapsack Problem
Journal of Heuristics
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
A hybrid approach for the 0-1 multidimensional knapsack problem
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Fuzzy social interaction genetic algorithm
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A social behaviour evolution approach for evolutionary optimisation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Structured population genetic algorithms: a literature survey
Artificial Intelligence Review
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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.