FUZZY EVOLUTIONARY CELLULAR AUTOMATA
J. Neal Richter, David Peak · 2002
An application of adaptive genetic algorithms to find optimal cellular automata rules to solve the density classification task is presented. A study of the statistical significance of previous results of the evolutionary cellular automata, EvCA, model is detailed, showing flaws in the fitness function. A brief review of recent work in advanced GAs and fuzzy-adaptive GAs is given. These techniques are then applied to the EvCA model to show improvement in convergence speed and more effective search of the optimization landscape.