Sun, Y, Omidvar, MN orcid.org/0000-0003-1944-4624, Kirley, M et al. (1 more author) (2018) Adaptive threshold parameter estimation with recursive differential grouping for problem decomposition. In: Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '18. The Genetic and Evolutionary Computation Conference, 15-19 Jul 2018, Kyoto. ACM Press , pp. 889-896. ISBN 9781450356183
Abstract
Problem decomposition plays an essential role in the success of cooperative co-evolution (CC), when used for solving large-scale optimization problems. The recently proposed recursive differential grouping (RDG) method has been shown to be very efficient, especially in terms of time complexity. However, it requires an appropriate parameter setting to estimate a threshold value in order to determine if two subsets of decision variables interact or not. Furthermore, using one global threshold value may be insufficient to identify variable interactions in components with different contribution to the fitness value. Inspired by the different grouping 2 (DG2) method, in this paper, we adaptively estimates a threshold value based on computational round-off errors for RDG. We derive an upper bound of the round-off errors, which is shown to be sufficient when identifying variable interactions across a wide range of large-scale benchmark problems. Comprehensive numerical experimental results showed that the proposed RDG2 method achieved higher decomposition accuracy than RDG and DG2. When embedded into a CC framework, it achieved statistically equal or significantly better solution quality than RDG and DG2, when used to solve the benchmark problems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018. Association for Computing Machinery. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Accounting & Finance Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Jan 2020 10:35 |
Last Modified: | 06 Feb 2020 23:22 |
Status: | Published |
Publisher: | ACM Press |
Identification Number: | 10.1145/3205455.3205483 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156235 |