Summary: Genetic Algorithms have been used before to evolve transition rules for one-dimensional Cellular Automata (CA) to solve e.g. the majority problem and investigate communication processes within such CA. In this paper, the principle is extended to multi-dimensional CA, and it is demonstrated how the approach evolves transition rules for the two-dimensional case with a von Neumann neighborhood. In particular, the method is applied to the binary AND and XOR problems by using the GA to optimize the corresponding rules. Moreover, it is shown how the approach can also be used for more general patterns, and therefore how it can serve as a method for calibrating and designing CA for real-world applications.
For the entire collection see [Zbl 1063.68011].