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A Fair Performance Comparison of Different Surrogate Optimization Strategies

  • Bernhard WerthEmail author
  • Erik Pitzer
  • Michael Affenzeller
Conference paper
  • 558 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10671)

Abstract

Much of the literature found on surrogate models presents new approaches or algorithms trying to solve black-box optimization problems with as few evaluations as possible. The comparisons of these new ideas with other algorithms are often very limited and constrained to non-surrogate algorithms or algorithms following very similar ideas as the presented ones. This work aims to provide both an overview over the most important general trends in surrogate assisted optimization and a more wide-spanning comparison in a fair environment by reimplementation within the same software framework.

Keywords

Surrogate models Evolutionary algorithms Black-box optimization 

Notes

Acknowledgements

This work was supported by the European Union through the European Regional Development Fund (EFRE; further information on IWB/EFRE is available at www.efre.gv.at).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Bernhard Werth
    • 1
    • 2
    Email author
  • Erik Pitzer
    • 1
  • Michael Affenzeller
    • 1
    • 2
  1. 1.Heuristic and Evolutionary Algorithms LaboratoryUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler UniversityLinzAustria

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