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Distributed Probabilistic Model-Building Genetic Algorithm

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2723))

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Abstract

In this paper, a new model of Probabilistic Model-Building Genetic Algorithms (PMBGAs), Distributed PMBGA (DPMBGA), is proposed. In the DPMBGA, the correlation among the design variables is considered by Principal Component Analysis (PCA) when the off-springs are generated. The island model is also applied in the DPMBGA for maintaining the population diversity. Through the standard test functions, some models of DPMBGA are examined. The DPMBGA where PCA is executed in the half of the islands can find the good solutions in the problems whether or not the problems have the correlation among the design variables. At the same time, the search capability and some characteristics of the DPMBGA are also discussed.

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Author information

Authors and Affiliations

  1. Doshisha University, Kyoto, Japan

    Tomoyuki Hiroyasu, Mitsunori Miki, Masaki Sano & Hisashi Shimosaka

  2. Hannan University, Osaka, Japan

    Shigeyoshi Tsutsui

  3. University of Tennessee, TN, USA

    Jack Dongarra

Authors
  1. Tomoyuki Hiroyasu
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  2. Mitsunori Miki
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  3. Masaki Sano
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  4. Hisashi Shimosaka
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  5. Shigeyoshi Tsutsui
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  6. Jack Dongarra
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Hiroyasu, T., Miki, M., Sano, M., Shimosaka, H., Tsutsui, S., Dongarra, J. (2003). Distributed Probabilistic Model-Building Genetic Algorithm. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_112

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  • DOI: https://doi.org/10.1007/3-540-45105-6_112

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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