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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2017: Evolutionary Multi-Criterion Optimization pp 670–684Cite as

Toward Step-Size Adaptation in Evolutionary Multiobjective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

Abstract

We give a definition for step size optimality in multiobjective optimization and visualize the optimal step sizes for a few two-dimensional example constellations. After that, we try to engineer a step size adaptation mechanism that also works in the real world. For this mechanism, we employ the self-adaptation of mutation strength, which is simple and well-known from single-objective optimization. The resulting approach obtains better results than simulated binary crossover and polynomial mutation on the bi-objective BBOB testbed.

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Notes

  1. 1.

    We are using step size and mutation strength synonymously in this work.

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Author information

Authors and Affiliations

  1. Computer Science Department, Technische Universität Dortmund, Otto-Hahn-Str. 14, 44221, Dortmund, Germany

    Simon Wessing, Rosa Pink, Kai Brandenbusch & Günter Rudolph

Authors
  1. Simon Wessing
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  2. Rosa Pink
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  3. Kai Brandenbusch
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  4. Günter Rudolph
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Corresponding author

Correspondence to Simon Wessing .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Wessing, S., Pink, R., Brandenbusch, K., Rudolph, G. (2017). Toward Step-Size Adaptation in Evolutionary Multiobjective Optimization. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_45

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_45

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  • Online ISBN: 978-3-319-54157-0

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