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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 2/2013

01-04-2013 | Original Article

Weighted preferences in evolutionary multi-objective optimization

Authors: Tobias Friedrich, Trent Kroeger, Frank Neumann

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2013

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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.
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Metadata
Title
Weighted preferences in evolutionary multi-objective optimization
Authors
Tobias Friedrich
Trent Kroeger
Frank Neumann
Publication date
01-04-2013
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2013
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0083-y

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