Weighted preferences in evolutionary multi-objective optimization

Authors:
Tobias Friedrich;Trent Kroeger;Frank Neumann
Affiliations:
Max-Planck-Institut für Informatik, Saarbrücken, Germany;School of Computer Science, University of Adelaide, Adelaide, Australia;School of Computer Science, University of Adelaide, Adelaide, Australia
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

Citing 15
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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.