Uncertainty quantification methods for evolutionary optimization under uncertainty

Pramudita Satria Palar, Koji Shimoyama, Lavi Rizki Zuhal

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

Abstract

In this paper, we discuss the role of uncertainty quantification (UQ) in assisting optimization under uncertainty. UQ plays a significant role in quantifying the robustness of solutions so as to help the optimizer in achieving robust optimum solutions. In this respect, the scientific discipline of UQ addresses various theoretical and practical aspects of uncertainty, which include representations of uncertainty and also efficient computation of the output uncertainty, to name a few. However, the UQ community and the evolutionary computation community rarely interact with each other despite the potential of utilizing the advancement in UQ for research in evolutionary computation. To that end, this paper serves as a short introduction to the science of UQ for the evolutionary computation community. We discuss several aspects of UQ for robust optimization such as aleatory and epistemic uncertainty and objective functions when uncertainties are considered. A tutorial on an aerodynamic design problem is also given to illustrate the use of UQ in a real-world problem.

Original languageEnglish
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1614-1622
Number of pages9
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - 2020 Jul 8
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: 2020 Jul 82020 Jul 12

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period20/7/820/7/12

Keywords

  • Evolutionary algorithm
  • Optimization under uncertainty
  • Uncertainty quantification

ASJC Scopus subject areas

  • Computational Mathematics

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