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Discussion of Search Strategy for Multi-objective Genetic Algorithm with Consideration of Accuracy and Broadness of Pareto Optimal Solutions

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Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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Abstract

In multi-objective optimization, it is important that the obtained solutions are high quality regarding accuracy, uniform distribution, and broadness. Of these qualities, we focused on accuracy and broadness of the solutions and proposed a search strategy. Since it is difficult to improve both convergence and broadness of the solutions at the same time in a multi-objective GA search, we considered to converge the solutions first and then broaden them in the proposed search strategy by dividing the search into two search stages. The first stage is to improve convergence of the solutions, and a reference point specified by a decision maker is adopted in this search. In the second stage, the solutions are broadened using the Distributed Cooperation Scheme. From the results of the numerical experiment, we found that the proposed search strategy is capable of deriving broader solutions than conventional multi-objective GA with equivalent accuracy.

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Author information

Authors and Affiliations

  1. Faculty of Life and Medical Sciences, Doshisha University, 1-3 Tatara Miyakodani Kyotanabe, Kyoto, Japan

    Tomoyuki Hiroyasu & Hisatake Yokouchi

  2. Graduate School of Engineering, Doshisha University, Japan

    Masashi Nishioka

  3. Faculty of Science and Engineering, Doshisha University, Japan

    Mitsunori Miki

Authors
  1. Tomoyuki Hiroyasu
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  2. Masashi Nishioka
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  3. Mitsunori Miki
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  4. Hisatake Yokouchi
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Hiroyasu, T., Nishioka, M., Miki, M., Yokouchi, H. (2008). Discussion of Search Strategy for Multi-objective Genetic Algorithm with Consideration of Accuracy and Broadness of Pareto Optimal Solutions. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_35

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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