Weighted preferences in evolutionary multi-objective optimization

  • Tobias Friedrich
  • Trent KroegerEmail author
  • Frank Neumann
Original Article


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.


Evolutionary algorithms Multi-objective optimization User preferences 


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Tobias Friedrich
    • 1
  • Trent Kroeger
    • 2
    Email author
  • Frank Neumann
    • 2
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.School of Computer ScienceThe University of AdelaideAdelaideAustralia

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