Doerr, B., Lissovoi, A., Oliveto, P.S. et al. (1 more author) (2018) On the runtime analysis of selection hyper-heuristics with adaptive learning periods. In: Aguirre, H.E. and Takadama, K., (eds.) GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference. GECCO '18 Genetic and Evolutionary Computation Conference, 15-19 Jul 2018, Kyoto, Japan. ACM , pp. 1015-1022. ISBN 978-1-4503-5618-3
Abstract
Selection hyper-heuristics are randomised optimisation techniques that select from a set of low-level heuristics which one should be applied in the next step of the optimisation process. Recently it has been proven that a Random Gradient hyper-heuristic optimises the LeadingOnes benchmark function in the best runtime achievable with any combination of its low-level heuristics, up to lower order terms. To achieve this runtime, the learning period τ, used to evaluate the performance of the currently chosen heuristic, should be set appropriately, i.e., super-linear in the problem size but not excessively larger. In this paper we automate the hyper-heuristic further by allowing it to self-adjust the learning period τ during the run. To achieve this we equip the algorithm with a simple self-adjusting mechanism, called 1 - o(1) rule, inspired by the 1/5 rule traditionally used in continuous optimisation. We rigorously prove that the resulting hyper-heuristic solves LeadingOnes in optimal runtime by automatically adapting τ and achieving a 1 - o(1) ratio of the desired behaviour. Complementary experiments for realistic problem sizes show the value of τ adapting as desired and that the hyper-heuristic with adaptive learning period outperforms the hyper-heuristic with fixed learning periods.
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: | © 2018 Authors / ACM. |
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: | 07 Aug 2018 09:18 |
Last Modified: | 07 Aug 2018 09:20 |
Published Version: | https://doi.org/10.1145/3205455.3205611 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3205455.3205611 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133965 |