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Neighborhood detection and rule selection from cellular automata patterns

Yang, Y.X. and Billings, S.A. (2000) Neighborhood detection and rule selection from cellular automata patterns. IEEE Transactions on Systems Man and Cybernetics Part A: Systems and Humans, 30 (6). pp. 840-847. ISSN 1083-4427


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Using genetic algorithms (GAs) to search for cellular automation (CA) rules from spatio-temporal patterns produced in CA evolution is usually complicated and time-consuming when both, the neighborhood structure and the local rule are searched simultaneously. The complexity of this problem motivates the development of a new search which separates the neighborhood detection from the GA search. In the paper, the neighborhood is determined by independently selecting terms from a large term set on the basis of the contribution each term makes to the next state of the cell to be updated. The GA search is then started with a considerably smaller set of candidate rules pre-defined by the detected neighhorhood. This approach is tested over a large set of one-dimensional (1-D) and two-dimensional (2-D) CA rules. Simulation results illustrate the efficiency of the new algorithm

Item Type: Article
Copyright, Publisher and Additional Information: Copyright © 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: cellular automata, genetic algorithms, identification, spatio-temporal systems
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Sherpa Assistant
Date Deposited: 02 Dec 2005
Last Modified: 12 Jun 2014 03:43
Published Version: http://dx.doi.org/10.1109/3468.895912
Status: Published
Refereed: Yes
Identification Number: 10.1109/3468.895912
URI: http://eprints.whiterose.ac.uk/id/eprint/795

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