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International Conference on Parallel Problem Solving from Nature

PPSN 2022: Parallel Problem Solving from Nature – PPSN XVII pp 3–17Cite as

Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13398))

Abstract

In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.

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Notes

  1. 1.

    https://github.com/Reiyan/pflacco_experiment.

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Author information

Authors and Affiliations

  1. Data Science: Statistics and Optimization, University of Münster, Münster, Germany

    Raphael Patrick Prager, Moritz Vinzent Seiler & Heike Trautmann

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

    Heike Trautmann

  3. Big Data Analytics in Transportation, TU Dresden, Dresden, Germany

    Pascal Kerschke

Authors
  1. Raphael Patrick Prager
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  2. Moritz Vinzent Seiler
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  3. Heike Trautmann
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  4. Pascal Kerschke
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Corresponding author

Correspondence to Raphael Patrick Prager .

Editor information

Editors and Affiliations

  1. TU Dortmund, Dortmund, Germany

    Günter Rudolph

  2. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  3. Shinshu University, Nagano, Japan

    Hernán Aguirre

  4. Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  5. University of Stirling, Stirling, UK

    Gabriela Ochoa

  6. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

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Prager, R.P., Seiler, M.V., Trautmann, H., Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13398. Springer, Cham. https://doi.org/10.1007/978-3-031-14714-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-14714-2_1

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  • Online ISBN: 978-3-031-14714-2

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