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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2023: Evolutionary Multi-Criterion Optimization pp 162–175Cite as

Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13970))

Abstract

Well-defined Hyper-Heuristics enhance the generalization of MOEAs and the blind usability on complex and even dynamic real-world application. Previous works already showed, that Hyper-Heuristics as selectors of crossover operators improve the performance of a single algorithm used on two opposing problem properties. In this paper, we present different selection mechanisms of Hyper-Heuristics, that are able to handle an expanded selection pool to cover more properties. We solve 20 benchmark problems with NSGA-II using those Hyper-Heuristics. By comparing the learning behaviour and the IGD trends of fixed crossover operator usages, we confirm that a combination of operators could outperform the best fixed operator. From the introduced Hyper-Heuristics in this paper, HHX-A made the best use of this advantage. It selects either all operators or a single operator alternately and learns fast which operators to prioritize to optimize the production. Due to periodic resets of the score, the Hyper-Heuristic is able to adapt fast to changes of the current state of the solving process. Although the pool is bigger and more diverse, we are able to show that HHX-A decides reasonably and fast. Therefore, it works well on a bigger set of problems with different properties.

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Authors and Affiliations

  1. Otto-von-Guericke University, Universitaetsplatz 2, Magdeburg, 39106, Germany

    Julia Heise & Sanaz Mostaghim

Authors
  1. Julia Heise
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  2. Sanaz Mostaghim
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Correspondence to Julia Heise .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  2. Leiden University, Leiden, The Netherlands

    André Deutz

  3. Leiden University, Leiden, The Netherlands

    Hao Wang

  4. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  5. TH Köln, Gummersbach, Germany

    Boris Naujoks

  6. University of Exeter, Exeter, UK

    Ke Li

  7. University of Jyvaskyla, Jyvaskyla, Finland

    Kaisa Miettinen

  8. De Montfort University, Leicester, UK

    Iryna Yevseyeva

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Heise, J., Mostaghim, S. (2023). Online Learning Hyper-Heuristics in Multi-Objective Evolutionary Algorithms. In: Emmerich, M., et al. Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol 13970. Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27249-3

  • Online ISBN: 978-3-031-27250-9

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