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Towards the efficient evolution of particle-based computation in cellular automata

Published: 12 July 2011 Publication History

Abstract

A fast compression based technique is proposed, capable of detecting promising emergent space-time patterns of cellular automata (CA). This information can be used to automatically guide the evolutionary search toward more complex, better performing rules. Results are presented for the most widely studied CA computation problem, the Density Classification Task (DCT), where incorporation of the proposed method almost always pushes the search beyond the simple block-expanding rules.

References

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M. Mitchell, J. P. Crutchfield, and R. Das. Evolving cellular automata with genetic algorithms: A review of recent work. In In Proceedings of EvCA'96, 1996.
[2]
H. Juille and J. Pollack. Coevolutionary learning and the design of complex systems. Advances in Complex Systems, 2(4):371--394, 2000.
[3]
G. M. B. Oliveira, J. C. Bortot, and P. P. B. de Oliveira. Further results on multiobjective evolutionary search for one-dimensional, density classifier, cellular automata, and strategy analysis of the rules. In LAPTEC 2007, volume 186, pages 133--159. IOS Press, 2007.

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cover image ACM Conferences
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
July 2011
1548 pages
ISBN:9781450306904
DOI:10.1145/2001858

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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  1. cellular automata
  2. density classification task

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