Learning cellular automata rules for binary classification problem

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, searchi...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 63; no. 3; pp. 800 - 815
Main Authors: Piwonska, Anna, Seredynski, Franciszek, Szaban, Miroslaw
Format: Journal Article
Language:English
Published: Boston Springer US 01.03.2013
Subjects:
Automation
Cellular
Cellular automata
Classification
Compilers
Computer Science
Genetic algorithms
Human
Interpreters
Learning
Processor Architectures
Programming Languages
Searching
Genetic algorithm
Binary classification problem
Cellular automata
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary: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.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-012-0767-9