Using a genetic algorithm to evolve behavior in multi dimensional cellular automata

Cellular automata are used in many fields to generate a global behavior with local rules. Finding the rules that display a desired behavior can be a hard task especially in real world problems. This paper proposes an improved approach to generate these transition rules for multi dimensional cellular automata using a genetic algorithm, thus giving a generic way to evolve global behavior with local rules, thereby mimicking nature. Three different problems are solved using multi dimensional topologies of cellular automata to show robustness, flexibility and potential. The results suggest that using multiple dimensions makes it easier to evolve desired behavior and that combining genetic algorithms with multi dimensional cellular automata is a very powerful way to evolve very diverse behavior and has great potential for real world problems.