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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
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 > Centre for Complexity Science Faculty of 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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