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Published in:
2011 | OriginalPaper | Chapter
Weighted Preferences in Evolutionary Multi-objective Optimization
Authors : Tobias Friedrich, Trent Kroeger, Frank Neumann
Published in: AI 2011: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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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.