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Transfer of Multi-objectively Tuned CMA-ES Parameters to a Vehicle Dynamics Problem

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Evolutionary Multi-Criterion Optimization (EMO 2023)

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

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Abstract

The conflict between computational budget and quality of found solutions is crucial when dealing with expensive black-box optimization problems from the industry. We show that through multi-objective parameter tuning of the Covariance Matrix Adaptation Evolution Strategy on benchmark functions different optimal algorithm configurations can be found for specific computational budgets and solution qualities. With the obtained Pareto front, tuned parameter sets are selected and transferred to a real-world optimization problem from vehicle dynamics, improving the solution quality and budget needed. The benchmark functions for tuning are selected based on their similarity to a real-world problem in terms of Exploratory Landscape Analysis features.

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Notes

  1. 1.

    HP Workstation Z4 G4 Intel Xeon W-2125 4.00 GHz/4.50 GHz 8.25MB 2666 4C 32 GB DDR4-2666 ECC SDRAM.

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Acknowledgements

This paper was written as part of the project newAIDE under the consortium leadership of BMW AG with the partners Altair Engineering GmbH, divis intelligent solutions GmbH, MSC Software GmbH, Technical University of Munich, TWT GmbH. The project is supported by the Federal Ministry for Economic Affairs and Climate Action (BMWK) on the basis of a decision of the German Bundestag.

The authors would like to thank Jacob de Nobel and Diederick Vermetten for their support with the modular CMA-ES implementation.

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Authors and Affiliations

  1. BMW AG, Munich, Germany

    André Thomaser & Marc-Eric Vogt

  2. LIACS, Leiden University, Leiden, The Netherlands

    André Thomaser, Anna V. Kononova & Thomas Bäck

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  1. André Thomaser
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  2. Marc-Eric Vogt
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  3. Anna V. Kononova
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  4. Thomas Bäck
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Corresponding author

Correspondence to André Thomaser .

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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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Thomaser, A., Vogt, ME., Kononova, A.V., Bäck, T. (2023). Transfer of Multi-objectively Tuned CMA-ES Parameters to a Vehicle Dynamics Problem. 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_39

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_39

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