This paper proposes a cellular automata-based solution of a two-dimensional binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an very good performance of discovered rules in solving the classification problem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Piwonska, A., Seredynski, F., & Szaban, M. (2012). Searching cellular automata rules for solving two-dimensional binary classification problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7495 LNCS, pp. 121–130). Springer Verlag. https://doi.org/10.1007/978-3-642-33350-7_13
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