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Balancing exploration and exploitation in multiobjective batch bayesian optimization

Published: 13 July 2019 Publication History

Abstract

Many applications such as hyper-parameter tunning in Machine Learning can be casted to multiobjective black-box problems and it is challenging to optimize them. Bayesian Optimization (BO) is an effective method to deal with black-box functions. This paper mainly focuses on balancing exploration and exploitation in multi-objective black-box optimization problems by multiple samplings in BBO. In each iteration, multiple recommendations are generated via two different trade-off strategies respectively the expected improvement (EI) and a multiobjective framework with the mean and variance function of the GP posterior forming two conflict objectives. We compare our algorithm with ParEGO by running on 12 test functions. Hypervolume (HV, also known as S-metric) results show that our algorithm works well in exploration-exploitation trade-off for multiobjective black-box optimization problems.

References

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K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (April 2002), 182--197.
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J. González, Z. Dai, P. Hennig, and N. Lawrence. 2016. Batch Bayesian Optimization via Local Penalization. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) (JMLR Workshop and Conference Proceedings), Vol. 51. 648--657.
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Sunil Gupta, Alistair Shilton, Santu Rana, and Svetha Venkatesh. 2018. Exploiting Strategy-Space Diversity for Batch Bayesian Optimization. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research), Vol. 84. PMLR, 538--547.
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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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Wolfgang Ponweiser, Tobias Wagner, Dirk Biermann, and Markus Vincze. 2008. Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted S-Metric Selection. In Parallel Problem Solving from Nature --- PPSN X. Springer Berlin Heidelberg, Berlin, Heidelberg, 784--794.
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Q. Zhang, W. Liu, E. Tsang, and B. Virginas. 2010. Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model. IEEE Transactions on Evolutionary Computation 14, 3 (June 2010), 456--474.
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Cited By

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  • (2023)A Batched Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-Fidelity ModelingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.317524142:2(347-359)Online publication date: Feb-2023
  • (2020)Automatic Discovery of Privacy–Utility Pareto FrontsProceedings on Privacy Enhancing Technologies10.2478/popets-2020-00602020:4(5-23)Online publication date: 17-Aug-2020

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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. batch bayesian optimization
  3. expensive multiobjective optimization
  4. exploration and exploitation
  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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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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View all
  • (2023)A Batched Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-Fidelity ModelingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.317524142:2(347-359)Online publication date: Feb-2023
  • (2020)Automatic Discovery of Privacy–Utility Pareto FrontsProceedings on Privacy Enhancing Technologies10.2478/popets-2020-00602020:4(5-23)Online publication date: 17-Aug-2020

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