Hall, G.T., Oliveto, P.S. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2020) Fast perturbative algorithm configurators. In: Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C. and Emm, M., (eds.) Parallel Problem Solving from Nature – PPSN XVI. International Conference on Parallel Problem Solving from Nature (PPSN 2020), 05-09 Sep 2020, Leiden, Netherlands. Lecture Notes in Computer Science, 12269 . Springer , pp. 19-32. ISBN 9783030581114
Abstract
Recent work has shown that the ParamRLS and ParamILS algorithm configurators can tune some simple randomised search heuristics for standard benchmark functions in linear expected time in the size of the parameter space. In this paper we prove a linear lower bound on the expected time to optimise any parameter tuning problem for ParamRLS, ParamILS as well as for larger classes of algorithm configurators. We propose a harmonic mutation operator for perturbative algorithm configurators that provably tunes single-parameter algorithms in polylogarithmic time for unimodal and approximately unimodal (i.e., non-smooth, rugged with an underlying gradient towards the optimum) parameter spaces. It is suitable as a general-purpose operator since even on worst-case (e.g., deceptive) landscapes it is only by at most a logarithmic factor slower than the default ones used by ParamRLS and ParamILS. An experimental analysis confirms the superiority of the approach in practice for a number of configuration scenarios, including ones involving more than one parameter.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author-produced version of a paper subsequently published in Bäck T. et al. (eds) Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science, vol 12269. 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: | 23 Jun 2020 07:40 |
Last Modified: | 10 Sep 2020 12:01 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-58112-1_2 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:162212 |