A new acquisition function for robust Bayesian optimization of unconstrained problems

Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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A new acquisition function is proposed for solving robust optimization problems via Bayesian Optimization. The proposed acquisition function reflects the need for the robust instead of the nominal optimum, and is based on the intuition of utilizing the higher moments of the improvement. The efficacy of Bayesian Optimization based on this acquisition function is demonstrated on four test problems, each affected by three different levels of noise. Our findings suggest the promising nature of the proposed acquisition function as it yields a better robust optimal value of the function in 6/12 test scenarios when compared with the baseline.

Original languageEnglish
Title of host publicationGECCO '21
Subtitle of host publicationProceedings of the Genetic and Evolutionary Computation Conference Companion
EditorsFrancisco Chicano
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Electronic)9781450383516
Publication statusPublished - 7 Jul 2021
Externally publishedYes
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: 10 Jul 202114 Jul 2021

Publication series

NameGenetic and Evolutionary Computation Conference (GECCO)
PublisherAssociation for Computing Machinery (ACM)


Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
CityVirtual, Online

Bibliographical note

Funding Information:
This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 766186 (ECOLE).

Publisher Copyright:
© 2021 ACM.


  • Bayesian optimization
  • kriging
  • robust optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics


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