Covantes Osuna, E. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2018) Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation. 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
Many real optimisation problems lead to multimodal domains and so require the identifi- cation of multiple optima. Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel and to reduce the effect of genetic drift. Using rigorous runtime analysis, we analyse for the first time two well known niching methods: probabilistic crowding and restricted tournament selection (RTS). We incorporate both methods into a (µ+1) EA on the bimodal function Twomax where the goal is to find two optima at opposite ends of the search space. In probabilistic crowding, the offspring compete with their parents and the survivor is chosen proportionally to its fitness. On Twomax probabilistic crowding fails to find any reasonable solution quality even in exponential time. In RTS the offspring compete against the closest individual amongst w (window size) individuals. We prove that RTS fails if w is too small, leading to exponential times with high probability. However, if w is chosen large enough, it finds both optima for Twomax in time O(µn log n) with high probability. Our theoretical results are accompanied by experimental studies that match the theoretical results and also shed light on parameters not covered by the theoretical results.
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. |
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 13:31 |
Last Modified: | 19 Dec 2022 13:49 |
Published Version: | https://dx.doi.org/10.1145/3205455.3205591 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3205455.3205591 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:130704 |