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Approximating Pareto-Optimal Sets Using Diversity Strategies in Evolutionary Multi-Objective Optimization

  • Christian Horoba
  • Frank Neumann
Part of the Studies in Computational Intelligence book series (SCI, volume 272)

Summary

Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such an approximation by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the ε -dominance approach and point out when and how such mechanisms provably help to obtain a good approximation of the Pareto-optimal set.

Keywords

Pareto Front Multiobjective Optimization Objective Space Diversity Strategy Search Point 
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 2010

Authors and Affiliations

  • Christian Horoba
    • 1
  • Frank Neumann
    • 2
  1. 1.Fakultät für Informatik, LS 2Technische Universität DortmundDortmundGermany
  2. 2.Algorithms and ComplexityMax-Planck-Institut für InformatikSaarbrückenGermany

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