Sharma, Mudita, López-Ibáñez, Manuel and Kazakov, Dimitar Lubomirov orcid.org/0000-0002-0637-8106 (2018) Performance Assessment of Recursive Probability Matching for Adaptive Operator Selection in Differential Evolution. In: 15th Intl Conf. on Parallel Problem Solving from Nature. LNCS . Springer , pp. 321-333.
Abstract
Probability Matching is one of the most successful methods for adaptive operator selection (AOS), that is, online parameter control, in evolutionary algorithms. In this paper, we propose a variant of Probability Matching, called Recursive Probability Matching (RecPM-AOS), that estimates reward based on progress in past generations and estimates quality based on expected quality of possible selection of operators in the past. We apply RecPM-AOS to the online selection of mutation strategies in differential evolution (DE) on the bbob benchmark functions. The new method is compared with two AOS methods, namely, PM-AdapSS, which utilises probability matching with relative fitness improvement, and F-AUC, which combines the concept of area under the curve with a multi-arm bandit algorithm. Experimental results show that the new tuned RecPM-AOS method is the most effective at identifying the best mutation strategy to be used by DE in solving most functions in bbob among the AOS methods.
Metadata
Item Type: | Book Section |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 11 Sep 2018 13:50 |
Last Modified: | 16 Oct 2024 10:58 |
Published Version: | https://doi.org/10.1007/978-3-319-99259-4_26 |
Status: | Published |
Publisher: | Springer |
Series Name: | LNCS |
Identification Number: | 10.1007/978-3-319-99259-4_26 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135483 |