Bali, K, Chandra, R and Omidvar, MN orcid.org/0000-0003-1944-4624 (2016) Contribution based multi-island competitive cooperative coevolution. In: 2016 IEEE Congress on Evolutionary Computation (CEC). 2016 IEEE Congress on Evolutionary Computation (CEC), 24-29 Jul 2016, Vancouver, BC, Canada. IEEE , pp. 1823-1830. ISBN 978-1-5090-0623-6
Abstract
Competition in cooperative coevolution (CC) has demonstrated success in solving global optimization problems. In a recent study, a multi-island competitive cooperative coevolution (MIC3) algorithm was introduced that featured competition and collaboration of several different problem decomposition strategies implemented as independent islands. It was shown that MIC3converges to high quality solutions without the need to find an optimal decomposition. MIC3splits the computational budget in terms of the number of function evaluations, equally amongst all the islands and evolves them in a round-robin fashion. This overlooks the difference in contributions of different islands towards improving the overall objective function value. Therefore, a considerable amount of function evaluations is wasted on the low-contributing islands as their problem decomposition strategies may not appeal to the problem at the given stage of the evolutionary process. This paper proposes contribution-based MIC3 algorithms (MIC4) that quantifies the contributions of each island and allocates the computational budget accordingly. The experimental analysis reveals that the proposed method outperforms its counterpart.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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 11:21 |
Last Modified: | 14 Feb 2020 21:48 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/cec.2016.7744010 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156238 |