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New sampling strategies when searching for robust solutions
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Fei, Xin, Branke, Jürgen and Gülpinar, Nalân (2019) New sampling strategies when searching for robust solutions. IEEE Transactions on Evolutionary Computation, 23 (2). pp. 273-287. doi:10.1109/TEVC.2018.2849331 ISSN 1089-778X.
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Official URL: https://doi.org/10.1109/TEVC.2018.2849331
Abstract
Many real-world optimisation problems involve un- certainties, and in such situations it is often desirable to identify robust solutions that perform well over the possible future scenarios. In this paper, we focus on input uncertainty, such as in manufacturing, where the actual manufactured product may differ from the specified design but should still function well. Estimating a solution’s expected fitness in such a case is challenging, especially if the fitness function is expensive to evaluate, and its analytic form is unknown. One option is to average over a number of scenarios, but this is computationally expensive. The archive sample approximation method reduces the required number of fitness evaluations by re-using previous evaluations stored in an archive. The main challenge in the application of this method lies in determining the locations of additional samples drawn in each generation to enrich the information in the archive and reduce the estimation error. In this paper, we use the Wasserstein distance metric to approximate the possible benefit of a potential sample location on the estimation error, and propose new sampling strategies based on this metric. Contrary to previous studies, we consider a sample’s contribution for the entire population, rather than inspecting each individual separately. This also allows us to dynamically adjust the number of samples to be collected in each generation. An empirical comparison with several previously proposed archive-based sample approximation methods demonstrates the superiority of our approaches.
Item Type: | Journal Article | ||||||||||
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Subjects: | Q Science > QA Mathematics T Technology > TS Manufactures |
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Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||||
Library of Congress Subject Headings (LCSH): | Mathematical optimization, Manufacturing processes -- Mathematical models | ||||||||||
Journal or Publication Title: | IEEE Transactions on Evolutionary Computation | ||||||||||
Publisher: | IEEE | ||||||||||
ISSN: | 1089-778X | ||||||||||
Official Date: | April 2019 | ||||||||||
Dates: |
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Volume: | 23 | ||||||||||
Number: | 2 | ||||||||||
Page Range: | pp. 273-287 | ||||||||||
DOI: | 10.1109/TEVC.2018.2849331 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||
Date of first compliant deposit: | 6 June 2018 | ||||||||||
Date of first compliant Open Access: | 7 June 2018 | ||||||||||
RIOXX Funder/Project Grant: |
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