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An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods

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Evolutionary Multi-Criterion Optimization (EMO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

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Abstract

Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phase. We demonstrate the ADM by comparing interactive versions of RVEA and NSGA-III on benchmark problems with up to 9 objectives. The experiments show that our ADM is efficient and allows repetitive testing to compare interactive evolutionary methods in a meaningful way.

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Acknowledgements

This research was partly funded by the Academy of Finland (grants 322221 and 311877) and is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO), https://www.jyu.fi/demo, at the University of Jyvaskyla. Ana B. Ruiz would like to thank the support of the Spanish government (project ECO2017-88883-R) and the Andalusian regional government (PAI group SEJ-532 and project UMA18-FEDERJA-024).

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Authors and Affiliations

  1. University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35 (Agora), FI-40014, University of Jyvaskyla, Finland

    Bekir Afsar & Kaisa Miettinen

  2. Department of Applied Economics (Mathematics), Universidad de Málaga, C/Ejido 6, 29071, Málaga, Spain

    Ana B. Ruiz

Authors
  1. Bekir Afsar
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  2. Kaisa Miettinen
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  3. Ana B. Ruiz
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Corresponding author

Correspondence to Bekir Afsar .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Afsar, B., Miettinen, K., Ruiz, A.B. (2021). An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_49

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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