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Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation

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Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12269))

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Abstract

We aim to observe differences between surrogate model assisted covariance matrix adaptation evolution strategies applied to simple test problems. We propose a simple Gaussian process assisted strategy as a baseline. The performance of the algorithm is compared with those of several related strategies using families of parameterized, unimodal test problems. The impact of algorithm design choices on the observed differences is discussed.

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Acknowledgements

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, B3H 4R2, Canada

    Lauchlan Toal & Dirk V. Arnold

Authors
  1. Lauchlan Toal
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  2. Dirk V. Arnold
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Corresponding author

Correspondence to Dirk V. Arnold .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  2. Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. Leiden University, Leiden, The Netherlands

    André Deutz

  4. Sorbonne University, Paris, France

    Hao Wang

  5. Sorbonne University, Paris, France

    Carola Doerr

  6. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  7. University of Münster, Münster, Germany

    Heike Trautmann

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Toal, L., Arnold, D.V. (2020). Simple Surrogate Model Assisted Optimization with Covariance Matrix Adaptation. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12269. Springer, Cham. https://doi.org/10.1007/978-3-030-58112-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-58112-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58111-4

  • Online ISBN: 978-3-030-58112-1

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