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Evolutionary Learning and Stability of Mixed-Rule Cellular Automata

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Neural Information Processing (ICONIP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8835))

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Abstract

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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Author information

Authors and Affiliations

  1. Hosei University, Koganei, Tokyo, 184-8584, Japan

    Ryo Sawayama & Toshimichi Saito

Authors
  1. Ryo Sawayama
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  2. Toshimichi Saito
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology Building, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Chu Kiong Loo

  2. Department of Electronics and Communication Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43009, Kajang, Selangor, Malaysia

    Keem Siah Yap

  3. School of Engineering and Information Technology, Murdoch University, South St., 6150, Murdoch, Western Australia, Australia

    Kok Wai Wong

  4. Department of Electrical and Electronics Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, 120-749, Seoul, South Korea

    Andrew Teoh

  5. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Ren’ai Road 111, SIP 215123, Suzhou, Jiangsu Province, China

    Kaizhu Huang

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© 2014 Springer International Publishing Switzerland

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Sawayama, R., Saito, T. (2014). Evolutionary Learning and Stability of Mixed-Rule Cellular Automata. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_33

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  • DOI: https://doi.org/10.1007/978-3-319-12640-1_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12639-5

  • Online ISBN: 978-3-319-12640-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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