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Pareto Evolution and Co-Evolution in Cognitive Neural Agents Synthesis for Tic-Tac-Toe
2007
2007 IEEE Symposium on Computational Intelligence and Games
Although a number of multi-objective evolutionary algorithms (MOEAs) have been proposed over the last two decades, very few studies have utilized MOEAs for game agent synthesis. Recently, we have suggested a co-evolutionary implementation using the Pareto Evolutionary Programming (PEP) algorithm. This paper describes a series of experiments using PEP for evolving artificial neural networks (ANNs) that act as game-playing agents. Three systems are compared: (i) a canonical PEP system, (ii) a
doi:10.1109/cig.2007.368113
dblp:conf/cig/YauTA07
fatcat:yy3645vkk5adrasot3opwqttf4