Evolutionary Methods for 2-D Cellular Automata Computation

This paper describes methods for evolving 2-D cellular automata to perform global computations. This is a difficult task because global behaviors must arise from local computations of many parallel cells. We present the results of numerous tests involving different genetic algorithm methods to perform the 2-D equivalent of classic 1-D CA tasks, including density classification and synchronization, and our own 2-D CA balanced surface minimization task. The performance of the GA was improved greatly by the use of totalistic CA rule tables, increasing the fidelity of fitness functions, and with coevolutionary techniques.