Nguyen, P.T.H. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2018) Memetic Algorithms Beat Evolutionary Algorithms on the Class of Hurdle Problems. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018). Genetic and Evolutionary Computation Conference (GECCO 2018), 15-19 Jul 2018, Kyoto, Japan. ACM ISBN 978-1-4503-5618-3
Abstract
Memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid exploitation and global optimisation. However, these algorithms are not well understood and the field is lacking a solid theoretical foundation that explains when and why memetic algorithms are effective. We provide a rigorous runtime analysis of a simple memetic algorithm, the (1+1) MA, on the Hurdle problem class, a landscape class of tuneable difficulty that shows a “big valley structure”, a characteristic feature of many hard problems from combinatorial optimisation. The only parameter of this class is the hurdle width w, which describes the length of fitness valleys that have to be overcome. We show that the (1+1) EA requires Θ(n w) expected function evaluations to find the optimum, whereas the (1+1) MA with best-improvement and first-improvement local search can find the optimum in Θ(n 2 +n 3/w2 ) and Θ(n 3/w2 ) function evaluations, respectively. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, the problem becomes easier for memetic algorithms. We discuss how these findings can explain and illustrate the success of memetic algorithms for problems with big valley structures.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. This is an author-produced version of a paper accepted for publication. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Evolutionary algorithms; hybridisation; iterated local search; local search; memetic algorithms; running time analysis; theory |
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: | 16 May 2018 13:26 |
Last Modified: | 19 Dec 2022 13:49 |
Published Version: | https://dx.doi.org/10.1145/3205455.3205456 |
Status: | Published online |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3205455.3205456 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130705 |