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Noisy multiobjective black-box optimization using bayesian optimization

Published: 13 July 2019 Publication History

Abstract

Expensive black-box problems are usually optimized by Bayesian Optimization (BO) since it can reduce evaluation costs via cheaper surrogates. The most popular model used in Bayesian Optimization is the Gaussian process (GP) whose posterior is based on a joint GP prior built by initial observations, so the posterior is also a Gaussian process. Observations are often not noise-free, so in most of these cases, a noisy transformation of the objective space is observed. Many single objective optimization algorithms have succeeded in extending efficient global optimization (EGO) to noisy circumstances, while ParEGO fails to consider noise. In order to deal with noisy expensive black-box problems, we extending ParEGO to noisy optimization according to adding a Gaussian noisy error while approximating the surrogate. We call it noisy-ParEGO and results of S-metric indicate that the algorithm works well on optimizing noisy expensive multiobjective black-box problems.

References

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T. Bartz-Beielstein, C. W. G. Lasarczyk, and M. Preuss. 2005. Sequential parameter optimization. In 2005 IEEE Congress on Evolutionary Computation, Vol. 1. 773--780 Vol.1.
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Alexander Forrester, Andras Sobester, and Andy Keane. 2008. Engineering design via surrogate modelling: a practical guide. Wiley, https://eprints.soton.ac.uk/64699/
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A. I. J. Forrester, A. J. Keane, and N. W. Bressloff. 2006. Design and Analysis of "Noisy" Computer Experiments. AIAA Journal 44 (Oct 2006), 2331--2339.
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D. Huang, T. T. Allen, W. I. Notz, and N. Zeng. 2006. Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models. Journal of Global Optimization 34, 3 (01 Mar 2006), 441--466.
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Donald R. Jones, Matthias Schonlau, and William J. Welch. 1998. Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization 13, 4 (01 Dec 1998), 455--492.
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J. Knowles. 2006. ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Transactions on Evolutionary Computation 10, 1 (Feb 2006), 50--66.
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B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas. 2016. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104, 1 (Jan 2016), 148--175.
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Cited By

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  • (2024)Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension ArchitectureIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32741005:2(904-915)Online publication date: Feb-2024
  • (2023)Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2021.311440353:4(2261-2274)Online publication date: Apr-2023

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 July 2019

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Author Tags

  1. ParEGO
  2. black-box optimization
  3. expensive multiobjective optimization
  4. gaussian noise
  5. gaussian process

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Cited By

View all
  • (2024)Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension ArchitectureIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32741005:2(904-915)Online publication date: Feb-2024
  • (2023)Kinematics Design of a MacPherson Suspension Architecture Based on Bayesian OptimizationIEEE Transactions on Cybernetics10.1109/TCYB.2021.311440353:4(2261-2274)Online publication date: Apr-2023

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