| An application of genetic algorithms in search of optimal cellular automata rules to solve the density classification task is presented. A review of recent work is detailed along with a study of the statistical significance of previous results. A review of powerful genetic algorithm enhancements is also presented, with the aim of demonstrating marked improvement in the robustness and optimization capability of the GA. These techniques are then applied to the evolutionary cellular automata model to show improvement in convergence speed and more effective search of the optimization landscape. |
