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A Fair Performance Comparison of Different Surrogate Optimization Strategies

Part of the Lecture Notes in Computer Science book series (LNTCS,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

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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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Correspondence to Bernhard Werth .

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Werth, B., Pitzer, E., Affenzeller, M. (2018). A Fair Performance Comparison of Different Surrogate Optimization Strategies. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_49

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  • DOI: https://doi.org/10.1007/978-3-319-74718-7_49

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