Learning cellular automata rules for binary classification problem - Piwonska, Anna; Seredynski, Franciszek; Szaban, Miroslaw (The Journal of supercomputing, 2013)
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.