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Evolution rules for Cellular Automata (CAs) able to perform computational tasks which require global coordination highlight an interesting emergent behavior. CAs can generate this complex behavior starting from a simple initial configuration based on the local interaction of simple components that evolve according to some state change rule. However, the detection of rules that exhibit coordinated global information processing is a very challenging task highly important in the study of complex systems.In this paper, we propose a new weighted rule for a cellular automaton with hybrid topology and neighborhood in which the state of a cell changes according to the cell itself and both local and long-distance cells. In the proposed approach, each cell in the neighborhood has a different weight (determined using an evolutionary algorithm) in the decision of changing the state for the current cell. Computational experiments focus on the well-known density classification task for the one-dimensional binary-state CA. Results support a better performance of the proposed weighted rule compared to the standard majority rule applied to the same CA topology.

2010 Mathematics Subject Classification. 68-02. 1998 CR Categories and Descriptors. I.2.8 Computing Methodologies [Artificial Intelligence]: Problem Solving, Control Methods, and Search – Heuristic methods.

Key words and phrases. evolutionary algorithms, density classification task, cellular automata.This paper has been presented at the International Conference KEPT2013: Knowledge Engineering Principles and Techniques, organized by Babes-Bolyai University, Cluj-Napoca, July 5-7 2013.