Coevolution of rules and topology in cellular automata

Coevolution is nature's response to highly complex and rapidly changing conditions. Biological systems are able to have multiple traits evolving concurrently to adapt to their environment. For many years, evolutionary algorithms have been successfully used on cellular automata (CA) to produce performant update functions. The resulting CAs are, however, much slower and more sensitive to perturbations than CAs with an evolved topology and fixed uniform update rule. Unfortunately, these are not nearly as performant, and suffer from scaling up the number of cells. We propose a hybrid paradigm that simultaneously coevolves the supporting network and the update functions of CAs. The resulting systems combine the high performance of the update evolution and the robustness properties and speed of the topology evolution CAs. Coevolution in CAs a viable tradeoff between the two single trait evolutions.