Duro, J.A. orcid.org/0000-0002-7684-4707, Oara, D.C., Sriwastava, A.K. et al. (3 more authors) (2021) Component-based design of multi-objective evolutionary algorithms using the Tigon optimization library. In: Chicano, F., (ed.) GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO '21 : The Genetic and Evolutionary Computation Conference, 10-14 Jul 2021, Lille, France. ACM Digital Library , pp. 1531-1539. ISBN 9781450383516
Abstract
Multi-objective optimization problems involve several conflicting objectives that have to be optimized simultaneously. Generating a complete Pareto-optimal front (POF) can be computationally expensive or even infeasible, and for that reason there has been an enormous interest in using multi-objective evolutionary algorithms (MOEAs), which are known to generate a good approximation of the POF. MOEAs can be difficult to implement, and even for experienced optimization experts it can be a very time consuming task. For this reason several optimization libraries exist in the literature, providing off-the-shelf access to the most popular MOEAs. Some optimization libraries also provide a framework to design MOEAs. However, existing frameworks can be too stringent and do not provide sufficient flexibility for the design of more sophisticated MOEAs. To address this, a recently proposed optimization library, known as Tigon, features a component-based framework for the design of MOEAs with a focus on flexibility and re-usability. This paper demonstrates the generality of this new framework by showing how to implement different types of MOEAs, covering several paradigms in evolutionary computation. The work in this paper serves as a guide for researchers, and others alike, to build their own MOEAs by using the Tigon optimization library.
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: | © 2021 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in Gecco'21 Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Software engineering; evolutionary algorithms; multi-objective optimization; algorithm design |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Innovate UK (TSB) 78938-506185 Engineering and Physical Science Research Council EP/L025760/1; EP/P504759/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 May 2021 06:51 |
Last Modified: | 14 Jul 2021 13:19 |
Status: | Published |
Publisher: | ACM Digital Library |
Refereed: | Yes |
Identification Number: | 10.1145/3449726.3463194 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173896 |