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Combining Gaussian processes, mutual information and a genetic algorithm for multi-target optimization of expensive-to-evaluate functions

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Peremezhney, Nicolai, Hines, Evor, Lapkin, Alexei and Connaughton, Colm (2014) Combining Gaussian processes, mutual information and a genetic algorithm for multi-target optimization of expensive-to-evaluate functions. Engineering Optimization, 46 (11). pp. 1593-1607. doi:10.1080/0305215X.2014.881997

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Official URL: http://dx.doi.org/10.1080/0305215X.2014.881997

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Abstract

A novel approach to multi-target optimization of expensive-to-evaluate functions is explored that is based on a combined application of Gaussian processes, mutual information and a genetic algorithm. The aim of the approach is to find an approximation to the optimal solution (or the Pareto optimal solutions) within a small budget. The approach is shown to compare favourably with a surrogate based online evolutionary algorithm on two synthetic problems.

Item Type: Journal Article
Divisions: Faculty of Science > Centre for Complexity Science
Faculty of Science > Physics
Journal or Publication Title: Engineering Optimization
Publisher: Routledge
ISSN: 0305-215X
Official Date: 28 February 2014
Dates:
DateEvent
28 February 2014Published
9 December 2013Accepted
Volume: 46
Number: 11
Page Range: pp. 1593-1607
DOI: 10.1080/0305215X.2014.881997
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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