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Type: Conference paper
Title: Weighted preferences in evolutionary multi-objective optimization
Author: Friedrich, T.
Kroeger, T.
Neumann, F.
Citation: AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings / D. Wang and M. Reynolds (eds.): pp.291-300
Publisher: Springer
Publisher Place: Germany
Issue Date: 2011
Series/Report no.: Lecture notes in Computer Science ; 7106
ISBN: 9783642258312
ISSN: 0302-9743
Conference Name: Australasian Joint Conference on Artificial Intelligence (24th : 2011 : Perth, Western Australia)
Editor: Wang, D.H.
Reynolds, M.
Statement of
Tobias Friedrich, Trent Kroeger, and Frank Neumann
Abstract: Evolutionary algorithms have been widely used to tackle multi-objective optimization problems. Incorporating preference information into the search of evolutionary algorithms for multi-objective optimization is of great importance as it allows one to focus on interesting regions in the objective space. Zitzler et al. have shown how to use a weight distribution function on the objective space to incorporate preference information into hypervolume-based algorithms. We show that this weighted information can easily be used in other popular EMO algorithms as well. Our results for NSGA-II and SPEA2 show that this yields similar results to the hypervolume approach and requires less computational effort.
Rights: © Springer-Verlag Berlin Heidelberg 2011
DOI: 10.1007/978-3-642-25832-9
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Appears in Collections:Aurora harvest
Computer Science publications

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