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https://hdl.handle.net/2440/71983
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Type: | Conference paper |
Title: | Approximation-guided evolutionary multi-objective optimization |
Author: | Bringmann, K. Friedrich, T. Neumann, F. Wagner, M. |
Citation: | Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, 16–22 July 2011 / Toby Walsh (ed.): pp.1198-1203 |
Publisher: | IJCAI |
Publisher Place: | online |
Issue Date: | 2011 |
ISBN: | 9781577355168 |
ISSN: | 1045-0823 |
Conference Name: | International Joint Conference on Artificial Intelligence (22nd : 2011 : Barcelona, Spain) |
Editor: | Walsh, T. |
Statement of Responsibility: | Karl Bringmann, Tobias Friedrich, Frank Neumann, Markus Wagner |
Abstract: | Multi-objective optimization problems arise frequently in applications but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a new framework of an evolutionary algorithm for multi-objective optimization that allows to work with a formal notion of approximation. Our experimental results show that our approach outperforms state-of-the-art evolutionary algorithms in terms of the quality of the approximation that is obtained in particular for problems with many objectives. |
Rights: | Copyright © 2011 International Joint Conferences on Artificial Intelligence |
DOI: | 10.5591/978-1-57735-516-8/IJCAI11-204 |
Published version: | http://ijcai.org/papers11/contents.php |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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