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Extending AεSεH from Many-objective to Multi-objective Optimization

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

This work analyzes the dynamics of dominance based multi-objective evolutionary algorithms and extends a many-objective evolutionary algorithm so that it can also work effectively in multi-objective problems. The many-objective algorithm incorporates in its selection mechanism a density sampling approach based on ε-dominance and performs recombination within neighborhoods created by another ε-dominance based procedure. The many-objective algorithm works well during the stage of the search where there are too many non-dominated solutions and dominance is not capable of ranking solutions. Here we modify the selection mechanism of the algorithm to also work effectively during the early stage of the search where dominance can be used to bias selection. This allows the algorithm to solve multi- or many-objective problems formulations using the same framework.

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References

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Shinshu University, 4-17-1 Wakasato, Nagano, 380-8553, Japan

    Hernán Aguirre, Yuki Yazawa & Kiyoshi Tanaka

  2. Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Japan

    Akira Oyama

Authors
  1. Hernán Aguirre
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  2. Yuki Yazawa
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  3. Akira Oyama
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  4. Kiyoshi Tanaka
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

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

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Aguirre, H., Yazawa, Y., Oyama, A., Tanaka, K. (2014). Extending AεSεH from Many-objective to Multi-objective Optimization. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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