A Parallel Multiobjective Approach to Evolving Cellular Automata Rules by Cell State Change Dynamics

The complex regimes of operation situated between ordered and chaotic behavior are hypothesized to give rise to computational capabilities. Lacking an universal blueprint for the emergence of complexity, a costly search is typically used to find the configurations of distributed artificial systems that can facilitate global computation. In this paper, we address the tedious task of searching for complex cellular automata rules able to lead to a certain global behavior based on local interactions. The discovery of rules exhibiting a high degree of global self-organization is of major importance in the study and understanding of complex systems. A classical heuristic search guided only by a coarse approximation of the ability of a rule to perform in certain conditions will generally not reach beyond an ordered regime of operation. To overcome this limitation, in this paper we incorporate a promising heuristic that rewards increased dynamics with regard to cell state changes in a multiobjective, parallel evolutionary framework. The scope of the multiobjective formulation is to balance the search between ordered and chaotic regimes in order to facilitate the discovery of rules exhibiting complex behaviors. Experimental results confirm that the combined approach represents an efficient way for supporting the emergence of complexity as in all runs we were able to find cellular automata exhibiting a high degree of global self-organization.