Presentation 2014-07-26
Learning of Mixed-Rule Cellular Automata Based on the Genetic Algorithm
Ryo SAWAYAMA, Toshimichi SAITO,
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Abstract(in English) This paper studies the cellular automaton (CA) governed by combination of two rules. First, we analyze a class of CA that generates several isolated spatiotemporal patterns without transient phenomena. Second, we present an evolutionary algorithm that tries to optimize the combination of two rules to stabilize the desired isolated patterns. Performing basic numerical experiments, it is shown that the evolutionary algorithm can make transient phenomena and can stabilize the desired isolated patterns.
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Keyword(in English) Cellular Automata / Genetic Algorism / Return Map
Paper # NC2014-19
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Committee NC
Conference Date 2014/7/19(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Learning of Mixed-Rule Cellular Automata Based on the Genetic Algorithm
Sub Title (in English)
Keyword(1) Cellular Automata
Keyword(2) Genetic Algorism
Keyword(3) Return Map
1st Author's Name Ryo SAWAYAMA
1st Author's Affiliation EE Dept., Hosei University()
2nd Author's Name Toshimichi SAITO
2nd Author's Affiliation EE Dept., Hosei University
Date 2014-07-26
Paper # NC2014-19
Volume (vol) vol.114
Number (no) 154
Page pp.pp.-
#Pages 5
Date of Issue