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Continuous multi-task Bayesian optimisation with correlation

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Pearce, Michael and Branke, Jürgen (2018) Continuous multi-task Bayesian optimisation with correlation. European Journal of Operational Research, 270 (3). pp. 1074-1085. doi:10.1016/j.ejor.2018.03.017

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Official URL: https://doi.org/10.1016/j.ejor.2018.03.017

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Abstract

This paper considers the problem of simultaneously identifying the optima for a (continuous or discrete) set of correlated tasks, where the performance of a particular input parameter on a particular task can only be estimated from (potentially noisy) samples. This has many applications, for example, identifying a stochastic algorithm’s optimal parameter settings for various tasks described by continuous feature values. We adapt the framework of Bayesian Optimisation to this problem. We propose a general multi-task optimisation framework and two myopic sampling procedures that determine task and parameter values for sampling, in order to efficiently find the best parameter setting for all tasks simultaneously. We show experimentally that our methods are much more efficient than collecting information randomly, and also more efficient than two other Bayesian multi-task optimisation algorithms from the literature.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Centre for Complexity Science
Library of Congress Subject Headings (LCSH): Heuristic algorithms, Stochastic analysis, Bayesian statistical decision theory
Journal or Publication Title: European Journal of Operational Research
Publisher: Elsevier Science BV
ISSN: 0377-2217
Official Date: 1 November 2018
Dates:
DateEvent
1 November 2018Published
1 April 2018Available
11 March 2018Accepted
Volume: 270
Number: 3
Page Range: pp. 1074-1085
DOI: 10.1016/j.ejor.2018.03.017
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIED[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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