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Abstract
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 language | English |
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Title of host publication | GECCO '21 |
Subtitle of host publication | Proceedings of the Genetic and Evolutionary Computation Conference Companion |
Editors | Francisco Chicano |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1344-1345 |
Number of pages | 2 |
ISBN (Electronic) | 9781450383516 |
DOIs | |
Publication status | Published - 7 Jul 2021 |
Externally published | Yes |
Event | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France Duration: 10 Jul 2021 → 14 Jul 2021 |
Publication series
Name | Genetic and Evolutionary Computation Conference (GECCO) |
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Publisher | Association for Computing Machinery (ACM) |
Conference
Conference | 2021 Genetic and Evolutionary Computation Conference, GECCO 2021 |
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Country/Territory | France |
City | Virtual, Online |
Period | 10/07/21 → 14/07/21 |
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.
Keywords
- Bayesian optimization
- kriging
- robust optimization
ASJC Scopus subject areas
- Computer Science Applications
- Software
- Computational Theory and Mathematics
Fingerprint
Dive into the research topics of 'A new acquisition function for robust Bayesian optimization of unconstrained problems'. Together they form a unique fingerprint.Projects
- 1 Finished
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H2020_ITN_ECOLE_Coordinator
Yao, X. (Principal Investigator)
European Commission, European Commission - Management Costs
1/04/18 → 31/03/22
Project: Research