Kazimipour, B, Omidvar, MN orcid.org/0000-0003-1944-4624, Qin, AK et al. (2 more authors) (2019) Bandit-based cooperative coevolution for tackling contribution imbalance in large-scale optimization problems. Applied Soft Computing, 76. pp. 265-281. ISSN 1568-4946
Abstract
This paper addresses the issue of computational resource allocation within the context of cooperative coevolution. Cooperative coevolution typically works by breaking a problem down into smaller subproblems (or components) and coevolving them in a round-robin fashion, resulting in a uniform resource allocation among its components. Despite its success on a wide range of problems, cooperative coevolution struggles to perform efficiently when its components do not contribute equally to the overall objective value. This is of crucial importance on large-scale optimization problems where such difference are further magnified. To resolve this imbalance problem, we extend the standard cooperative coevolution to a new generic framework capable of learning the contribution of each component using multi-armed bandit techniques. The new framework allocates the computational resources to each component proportional to their contributions towards improving the overall objective value. This approach results in a more economical use of the limited computational resources. We study different aspects of the proposed framework in the light of extensive experiments. Our empirical results confirm that even a simple bandit-based credit assignment scheme can significantly improve the performance of cooperative coevolution on large-scale continuous problems, leading to competitive performance as compared to the state-of-the-art algorithms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier B.V. All rights reserved. This is an author produced version of an article published in Applied Soft Computing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Imbalanced large-scale optimization; Resource allocation; Multi-armed bandits |
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: | 03 Feb 2020 14:24 |
Last Modified: | 11 Mar 2020 23:18 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.asoc.2018.12.007 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156297 |