Neighbourhood Detection and Rule Selection From Cellular Automata Patterns
Yang, Y.X. and Billings, S.A.
(1999)
Neighbourhood Detection and Rule Selection From Cellular Automata Patterns.
Research Report.
ACSE Research Report 742
.
Department of Automatic Control and Systems Engineering
Abstract
Using GA's to search for CA rules from spatio-temporal patterns produced in CA evolution is usually complicated and time-consuming when both the neighbourhood structure and the local rule are searched simultaneously. The complexity of this problem motivates the development of a new search which separates the neighbourhood detection from the GA search. In this paper, the neighbourhood is determined by independently selecting certain 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 per-defined by the detected neighbourhood. This approach is tested over a large set of 1-D and 2-D rules. Simulation results illustrate the efficiency of the new algorithm.
Metadata
Item Type:
Monograph
Authors/Creators:
Yang, Y.X.
Billings, S.A.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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