Corus, D., Dang, D.C., Eremeev, A.V. et al. (1 more author) (2014) Level-Based Analysis of Genetic Algorithms and Other Search Processes. In: Parallel Problem Solving from Nature – PPSN XIII. The 13th PPSN: International Conference on Parallel Problem Solving from Nature, September 13-17, 2014, Ljubljana, Slovenia. Lecture Notes in Computer Science (8672). Springer , pp. 912-921.
Abstract
The fitness-level technique is a simple and old way to derive upper bounds for the expected runtime of simple elitist evolutionary algorithms (EAs). Recently, the technique has been adapted to deduce the runtime of algorithms with non-elitist populations and unary variation operators [2,8]. In this paper, we show that the restriction to unary variation operators can be removed. This gives rise to a much more general analytical tool which is applicable to a wide range of search processes. As introductory examples, we provide simple runtime analyses of many variants of the Genetic Algorithm on well-known benchmark functions, such as OneMax, LeadingOnes, and the sorting problem.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Springer. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-10762-2_90 |
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 Aug 2017 09:26 |
Last Modified: | 21 Mar 2018 17:36 |
Published Version: | https://doi.org/10.1007/978-3-319-10762-2_90 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-10762-2_90 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120164 |