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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 . doi:10.1287/opre.2021.2208 (In Press)

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Official URL: https://doi.org/10.1287/opre.2021.2208

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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
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: 2022
Dates:
DateEvent
2022Published
3 March 2022Available
28 July 2021Accepted
DOI: 10.1287/opre.2021.2208
Status: Peer Reviewed
Publication Status: In Press
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: Copyright © 2022, INFORMS
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/101358X/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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