Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization

  • Tobias Friedrich
  • Christian Horoba
  • Frank Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


It is widely assumed that evolutionary algorithms for multi-objective optimization problems should use certain mechanisms to achieve a good spread over the Pareto front. In this paper, we examine such mechanisms from a theoretical point of view and analyze simple algorithms incorporating the concept of fairness introduced by Laumanns et al.[7]. This mechanism tries to balance the number of offspring of all individuals in the current population. We rigorously analyze the runtime behavior of different fairness mechanisms and present showcase examples to point out situations where the right mechanism can speed up the optimization process significantly.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tobias Friedrich
    • 1
  • Christian Horoba
    • 2
  • Frank Neumann
    • 1
  1. 1.Max-Planck-Institut für InformatikSaarbrückenGermany
  2. 2.Fakultät für Informatik, LS 2TU DortmundDortmundGermany

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