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Modification of Local Search Directions for Non-dominated Solutions in Cellular Multiobjective Genetic Algorithms for Pattern Classification Problems

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

Hybridization of evolutionary algorithms with local search (LS) has already been investigated in many studies. Such a hybrid algorithm is often referred to as a memetic algorithm. Hart investigated the following four questions for designing efficient memetic algorithms for single-objective optimization: (1) How often should LS be applied? (2) On which solutions should LS be used? (3) How long should LS be run? (4) How efficient does LS need to be? When we apply LS to an evolutionary multiobjective optimization (EMO) algorithm, another question arises: (5) To which direction should LS drive? This paper mainly addresses the final issue together with the others. We apply LS to the set of non-dominated solutions that is stored separately from the population governed by genetic operations in a cellular multiobjective genetic algorithm (C-MOGA). The appropriate direction for the non-dominated solutions is attained in experiments on multiobjective classification problems.

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Author information

Authors and Affiliations

  1. Department of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki, Osaka, 569-1095, Japan

    Tadahiko Murata

  2. Department of Industrial and Information Systems Engineering, Ashikaga Institute of Technology, 268 Omae-cho, Ashikaga, Tochigi, 326-8558, Japan

    Hiroyuki Nozawa

  3. Department of Industrial Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8531, Japan

    Hisao Ishibuchi

  4. Graduate School of Information, Production and Systems, Waseda University, 2-2 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan

    Mitsuo Gen

Authors
  1. Tadahiko Murata
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  2. Hiroyuki Nozawa
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  3. Hisao Ishibuchi
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  4. Mitsuo Gen
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Murata, T., Nozawa, H., Ishibuchi, H., Gen, M. (2003). Modification of Local Search Directions for Non-dominated Solutions in Cellular Multiobjective Genetic Algorithms for Pattern Classification Problems. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_42

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  • DOI: https://doi.org/10.1007/3-540-36970-8_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

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