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Bayesian optimization allowing for common random numbers
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Pearce , Michael, Policzek, Matthias and Branke, Jürgen (2022) Bayesian optimization allowing for common random numbers. Operations Research, 70 (6). pp. 3457-3472. doi:10.1287/opre.2021.2208 ISSN 0030-364X.
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WRAP-bayesian-optimization-allowing-common-random-numbers-Branke-2021.pdf - Accepted Version - Requires a PDF viewer. Download (3076Kb) | Preview |
Official URL: https://doi.org/10.1287/opre.2021.2208
Abstract
We consider the problem of stochastic simulation optimization with common random numbers over a numerical search domain. We propose the Knowledge Gradient for Common Random Numbers (KG-CRN) sequential sampling algorithm, a simple elegant modification to the Knowledge Gradient that incorporates the use of correlated noise in simulation outputs with Gaussian Process meta-models. We compare this method against the standard Knowledge Gradient and a more recently proposed variation that allows for pairwise sampling. Our method significantly outperforms both baselines under identical laboratory conditions while greatly reducing computational cost compared to pairwise sampling.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics | ||||||||
| Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
| Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Gaussian processes, Numbers, Random | ||||||||
| Journal or Publication Title: | Operations Research | ||||||||
| Publisher: | INFORMS | ||||||||
| ISSN: | 0030-364X | ||||||||
| Official Date: | November 2022 | ||||||||
| Dates: |
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| Volume: | 70 | ||||||||
| Number: | 6 | ||||||||
| Page Range: | pp. 3457-3472 | ||||||||
| DOI: | 10.1287/opre.2021.2208 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Copyright Holders: | Copyright © 2022, INFORMS | ||||||||
| Date of first compliant deposit: | 7 September 2021 | ||||||||
| Date of first compliant Open Access: | 7 April 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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