Covantes Osuna, E. and Sudholt, D. orcid.org/0000-0001-6020-1646 (2017) Analysis of the Clearing Diversity-Preserving Mechanism. In: FOGA '17 Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA '17), 12-15 Jan 2017, Copenhagen, Denmark. Association for Computing Machinery . ISBN 978-1-4503-4651-1
Abstract
Clearing is a niching method inspired by the principle of assigning the available resources among a subpopulation to a single individual. The clearing procedure supplies these resources only to the best individual of each subpopulation: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity mechanism. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and runtime analysis to explain how and why it is a powerful method. We prove that a (mu+1) EA with large enough population size and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that a (mu+1) EA with phenotypic and genotypic distances is able to find both optima in TWOMAX for large niches in polynomial expected time.
Metadata
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Copyright, Publisher and Additional Information: | © 2017 ACM. This is an author produced version of a paper subsequently published in FOGA '17 Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Uploaded in accordance with the publisher's self-archiving policy. | ||||
Keywords: | Clearing; diversity-preserving mechanisms; evolutionary algorithm; runtime analysis | ||||
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) | ||||
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 12 Apr 2017 09:34 | ||||
Last Modified: | 21 Jul 2017 08:12 | ||||
Published Version: | https://doi.org/10.1145/3040718.3040731 | ||||
Status: | Published | ||||
Publisher: | Association for Computing Machinery | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1145/3040718.3040731 |