Weighted Preferences in Evolutionary Multi-objective Optimization

  • Tobias Friedrich
  • Trent Kroeger
  • Frank Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

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.

Keywords

Pareto Front Multiobjective Optimization Objective Space Objective Vector Hypervolume Indicator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

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