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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 ISSN 0305-215X.
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Official URL: http://dx.doi.org/10.1080/0305215X.2014.881997
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 | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Research Centres > Centre for Complexity Science Faculty of Science, Engineering and Medicine > Science > Physics |
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Journal or Publication Title: | Engineering Optimization | ||||||
Publisher: | Routledge | ||||||
ISSN: | 0305-215X | ||||||
Official Date: | 28 February 2014 | ||||||
Dates: |
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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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