Hall, G.T., Oliveto, P.S. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2020) Analysis of the performance of algorithm configurators for search heuristics with global mutation operators. In: Coello Coello, C.A., (ed.) GECCO 2020: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '20: Genetic and Evolutionary Computation Conference, 08-12 Jul 2020, Cancún Mexico (Online). ACM Digital Library , pp. 823-831. ISBN 9781450371285
Abstract
Recently it has been proved that a simple algorithm configurator called ParamRLS can efficiently identify the optimal neighbourhood size to be used by stochastic local search to optimise two standard benchmark problem classes. In this paper we analyse the performance of algorithm configurators for tuning the more sophisticated global mutation operator used in standard evolutionary algorithms, which flips each of the n bits independently with probability χ/n and the best value for χ has to be identified. We compare the performance of configurators when the best-found fitness values within the cutoff time k are used to compare configurations against the actual optimisation time for two standard benchmark problem classes, Ridge and LeadingOnes. We rigorously prove that all algorithm configurators that use optimisation time as performance metric require cutoff times that are at least as large as the expected optimisation time to identify the optimal configuration. Matters are considerably different if the fitness metric is used. To show this we prove that the simple ParamRLS-F configurator can identify the optimal mutation rates even when using cutoff times that are considerably smaller than the expected optimisation time of the best parameter value for both problem classes.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. This is an author-produced version of a paper subsequently published in GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Parameter tuning; Algorithm configurators; Runtime analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Apr 2020 10:52 |
Last Modified: | 25 May 2023 14:36 |
Status: | Published |
Publisher: | ACM Digital Library |
Refereed: | Yes |
Identification Number: | 10.1145/3377930.3390218 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159939 |