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User Preferences for Approximation-Guided Multi-objective Evolution

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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

Incorporating user preferences into evolutionary multi-objective evolutionary algorithms has been an important topic in recent research in the area of evolutionary multi-objective optimization. We present a very simple and yet very effective modification to the Approximation-Guided Evolution (AGE) algorithm to incorporate user preferences. Over a wide range of test functions, we observed that the resulting algorithm called iAGE is just as good at finding evenly distributed solutions as similarly modified NSGA-II and SPEA2 variants. However, in particular for ”difficult” two-objective problems and for all three-objective problems we see more evenly distributed solutions in the user preferred region when using iAGE.

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Author information

Authors and Affiliations

  1. Optimisation and Logistics, School of Computer Science, The University of Adelaide, Australia

    Anh Quang Nguyen, Markus Wagner & Frank Neumann

Authors
  1. Anh Quang Nguyen
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  2. Markus Wagner
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  3. Frank Neumann
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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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Nguyen, A.Q., Wagner, M., Neumann, F. (2014). User Preferences for Approximation-Guided Multi-objective Evolution. 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_22

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_22

  • 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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