Playing Games with Genetic Algorithms

Abstract. 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 gameplaying 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.

Evolutionary models in economics: a survey of methods and building blocks

Agent based models, Diversity, Evolutionary computation, Evolutionary game theory, Growth mechanisms, Innovation, Multilevel evolution, Neo-Schumpeterian models, Selection dynamics, B52, C60, C73,