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The difficulty of designing cellular automatons' transition rules to perform a particular problem has severely limited their applications. Using a genetic algorithm to evolve cellular automata for fining these rules, is a good solution. Conventional evolutionary methods use random test configurations for calculating fitness values of each transition rule. In this paper, we use Principal Component Analysis to build better test configurations. By emphasizing on diversity between test instances indoi:10.1145/1389095.1389312 dblp:conf/gecco/NajafiB08 fatcat:qu2hnf74srh6pjne5tcmsydr4e