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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Unknown
Subjects: Computer Science - Computer Science and Game Theory, Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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