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On Properties of Genetic Operators from a Network Analytical Viewpoint

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4234))

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Abstract

In recent years, network analysis has revealed that some real networks have the properties of small-world and/or scale-free networks. In this paper, a simple Genetic Algorithm (GA) is regarded as a network where each node and each edge respectively represent a population and the possibility of the transition between two nodes. The characteristic path length, which is one of the most popular criterion in small-world networks, is derived analytically. The results show how the crossover operation works in GAs to shorten the path length between two populations, compared to the length of the network with the mutation operation.

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Author information

Authors and Affiliations

  1. Department of Systems, Kyoto University, Kyoto, 606-8501, Japan

    Hiroyuki Funaya & Kazushi Ikeda

Authors
  1. Hiroyuki Funaya
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  2. Kazushi Ikeda
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, The Chinese Univ. of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong, New Territories,, China

    Jun Wang

  3. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Lai-Wan Chan

  4. Department of Computer Science and Engineering & Center for Cognitive Science, The Ohio State University, OH 43210, Columbus

    DeLiang Wang

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© 2006 Springer-Verlag Berlin Heidelberg

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Funaya, H., Ikeda, K. (2006). On Properties of Genetic Operators from a Network Analytical Viewpoint. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_82

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  • DOI: https://doi.org/10.1007/11893295_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46484-6

  • Online ISBN: 978-3-540-46485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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