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A Real-coded Genetic Algorithm using the Unimodal Normal Distribution Crossover

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Advances in Evolutionary Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

This chapter presents a real-coded genetic algorithm using the Unimodal Normal Distribution Crossover (UNDX) that can efficiently optimize functions with epistasis among parameters. Most conventional crossover operators for function optimization have been reported to have a serious problem in that their performance deteriorates considerably when they are applied to functions with epistasis among parameters. We believe that the reason for the poor performance of the conventional crossover operators is that they cannot keep the distribution of individuals unchanged in the process of repetitive crossover operations on functions with epistasis among parameters. In considering the above problem, we introduce three guidelines, ‘Preservation of Statistics’, ‘Diversity of Offspring’, and ‘Enhancement of Robustness’, for designing crossover operators that show good performance even on epistatic functions. We show that the UNDX meets the guidelines very well by a theoretical analysis and that the UNDX shows better performance than some conventional crossover operators by applying them to some benchmark functions including multimodal and epistatic ones. We also discuss some improvements of the UNDX under the guidelines and the relation between real-coded genetic algorithms using the UNDX and evolution strategies (ESs) using the correlated mutation.

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Author information

Authors and Affiliations

  1. Faculty of Engineering, The University of Tokushima, 2-1, Minami-Josanjima, Tokushima, 770-8506, Japan

    Isao Ono

  2. Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259, Nagatsuta, Midori-ku, Yokohama, 226-8502, Japan

    Hajime Kita & Shigenobu Kobayashi

Authors
  1. Isao Ono
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  2. Hajime Kita
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  3. Shigenobu Kobayashi
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Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700 035, India

    Ashish Ghosh

  2. Department of Management Information, Hannan University, 5-4-33 Amamhigigashi, Matsubara, Osaka, 580-8502, Japan

    Shigeyoshi Tsutsui

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© 2003 Springer-Verlag Berlin Heidelberg

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Ono, I., Kita, H., Kobayashi, S. (2003). A Real-coded Genetic Algorithm using the Unimodal Normal Distribution Crossover. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-18965-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62386-8

  • Online ISBN: 978-3-642-18965-4

  • eBook Packages: Springer Book Archive

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