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On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems

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Applications of Evolutionary Computation (EvoApplications 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14634))

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Abstract

The complexity of Multi-Objective (MO) continuous optimisation problems arises from a combination of different characteristics, such as the level of multi-modality. Earlier studies revealed that there is a conflict between solver convergence in objective space and solution set diversity in the decision space, which is especially important in the multi-modal setting. We build on top of this observation and investigate this trade-off in a multi-objective manner by using multi-objective automated algorithm configuration (MO-AAC) on evolutionary multi-objective algorithms (EMOA). Our results show that MO-AAC is able to find configurations that outperform the default configuration as well as configurations found by single-objective AAC in regards to objective space convergence and diversity in decision space, leading to new recommendations for high-performing default settings.

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Notes

  1. 1.

    Source code: https://github.com/jeroenrook/SMAC3/tree/mosmac-anon.

  2. 2.

    Experimental code can be found at https://github.com/jeroenrook/MMMOO-moconfig-exp.

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Authors and Affiliations

  1. Machine Learning and Optimisation, Paderborn University, Paderborn, Germany

    Oliver Ludger Preuß & Heike Trautmann

  2. Data Management and Biometrics, University of Twente, Enschede, The Netherlands

    Jeroen Rook & Heike Trautmann

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  1. Oliver Ludger Preuß
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  2. Jeroen Rook
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Correspondence to Oliver Ludger Preuß .

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Editors and Affiliations

  1. University of York, York, UK

    Stephen Smith

  2. University of Coimbra, Coimbra, Portugal

    João Correia

  3. University of Málaga, Málaga, Spain

    Christian Cintrano

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Preuß, O.L., Rook, J., Trautmann, H. (2024). On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14634. Springer, Cham. https://doi.org/10.1007/978-3-031-56852-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-56852-7_20

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