Using PCA to improve evolutionary cellular automata algorithms

Mehran Najafi, Hamid Beigy
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
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 in
more » ... a test plan, we can evaluate rules better and faster as well as increase their accuracy. In this paper, we propose two models based on this idea. Experimental results on density classification and synchronization tasks prove that our methods are more efficient than the conventional one.
doi:10.1145/1389095.1389312 dblp:conf/gecco/NajafiB08 fatcat:qu2hnf74srh6pjne5tcmsydr4e