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Robust Optimization by ε-Ranking on High Dimensional Objective Spaces

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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

This work proposes a method to fine grain the ranking of solutions after they have been ranked by Pareto dominance, aiming to improve the performance of evolutionary algorithms on many objectives optimization problems. The re-ranking method uses a randomized sampling procedure to choose, from sets of equally ranked solutions, those solutions that will be given selective advantage. The sampling procedure favors a good distribution of the sampled solutions based on dominance regions wider than conventional Pareto dominance. We enhance NSGA-II with the proposed method and test its performance on MNK-Landscapes with up to M = 10 objectives. Experimental results show that convergence and diversity of the solutions found can improve remarkably on 3 ≤ M ≤ 10 objectives problems.

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References

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Author information

Authors and Affiliations

  1. Fiber-Nanotech Young Researcher Empowerment Program, Japan

    Hernán Aguirre

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

    Hernán Aguirre & Kiyoshi Tanaka

Authors
  1. Hernán Aguirre
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  2. Kiyoshi Tanaka
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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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Aguirre, H., Tanaka, K. (2008). Robust Optimization by ε-Ranking on High Dimensional Objective Spaces. 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_43

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_43

  • 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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