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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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:	Date Event 2022 Published 3 March 2022 Available 28 July 2021 Accepted
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 ID RIOXX Funder Name Funder ID EP/101358X/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
Related URLs:	Publisher
Open Access Version:	ArXiv

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