Cellular Automata Coevolution of Update Functions and Topologies: A Tradeoff between Accuracy and Speed

Abstract

Biological organisms have the ability to develop novel phenotypes in response to environmental changes. When several traits are evolved simultaneously or as a result of one another, we talk of coevolution. Cellular Automata (CAs) have been successfully used to artificially evolve problem specific update functions. The resulting CAs are, however, much slower and more sensitive to perturbations than those with an evolved underlying topology and fixed uniform update rule. Unfortunately, these are not nearly as accurate, and suffer from scaling up the total 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 higher fitness and performance of the update evolution and the robustness properties and speed of the topology evolution CAs. Moreover, these systems seem to perform better as the size of the CA scales up, where as single-feature evolution systems are negatively impacted. Coevolution in CAs is an interesting tradeoff between the two single trait evolutions.