Lissovoi, A., Sudholt, D. orcid.org/0000-0001-6020-1646, Wagner, M. et al. (1 more author) (2017) Theoretical results on bet-and-run as an initialisation strategy. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017). Genetic and Evolutionary Computation Conference (GECCO 2017), 15/07/2017 - 19/07/2017, Berlin. ACM , pp. 857-864.
Abstract
Bet-and-run initialisation strategies have been experimentally shown to be beneficial on classical NP-complete problems such as the travelling salesperson problem and minimum vertex cover. We analyse the performance of a bet-and-run restart strategy, where k independent islands run in parallel for t1 iterations, after which the optimisation process continues on only the best-performing island. We define a family of pseudo-Boolean functions, consisting of a plateau and a slope, as an abstraction of real fitness landscapes with promising and deceptive regions. The plateau shows a high fitness, but does not allow for further progression, whereas the slope has a low fitness initially, but does lead to the global optimum. We show that bet-and-run strategies with non-trivial k and t1 are necessary to find the global optimum efficiently. We show that the choice of t1 is linked to properties of the function. Finally, we provide a fixed budget analysis to guide selection of the bet-and-run parameters to maximise expected fitness after t = k · t1 + t2 fitness evaluations.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Copyright held by the owner / author(s). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). |
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) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 SAGE - 138086 European Cooperation in Science and Technology CA15140 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 May 2017 11:30 |
Last Modified: | 18 Jul 2017 10:04 |
Published Version: | https://doi.org/10.1145/3071178.3071329 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3071178.3071329 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:115997 |