Learning cellular automata rules for binary classification problem

Authors:
Anna Piwonska;Franciszek Seredynski;Miroslaw Szaban
Affiliations:
Computer Science Faculty, Bialystok University of Technology, Bialystok, Poland;Poland and Polish-Japanese Institute of Information Technology, Cardinal Stefan Wyszynski University, Warsaw, Poland;Institute of Computer Science, University of Natural Sciences and Humanities, Siedlce, Poland
Venue:
The Journal of Supercomputing
Year:
2013

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Abstract

This paper proposes a cellular automata-based solution of a 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 excellent 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.