Cellular Automata Pattern Recognition and Rule Evolution Through a Neuro-Genetic Approach
Cellular Automata rules often produce spatial patterns which make them recognizable by human observers. Nevertheless, it is generally difficult, if not impossible, to identify the characteristic(s) that make a rule produce a particular pattern. Discovering rules that produce spatial patterns that a human being would find "similar" to another given pattern is a very important task, given its numerous possible applications in many complex systems models. In this paper, we propose a general framework to accomplish this task, based on a combination of Machine Learning strategies including Genetic Algorithms and Artificial Neural Networks. This framework is tested on a 3-values, 6-neighbors, k-totalistic cellular automata rule called the "burning paper" rule. Results are encouraging and should pave the way for the use of our framework in real-life complex systems models.
pattern recognition rule evolution spatial patterns machine learning genetic algorithms neural networks hybrid learning systems