A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets

  • Günter RudolphEmail author
  • Oliver Schütze
  • Christian Grimme
  • Heike Trautmann
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 288)


The incorporation of expert knowledge into multiobjective optimization is an important issue which in this paper is reflected in terms of an aspiration set consisting of multiple reference points. The behaviour of the recently introduced evolutionary multiobjective algorithm AS-EMOA is analysed in detail and comparatively studied for bi-objective optimization problems w.r.t. R-NSGA2 and a respective variant. It will be shown that the averaged Hausdorff distance, integrated into AS-EMOA, is an efficient means to accurately approximate the desired aspiration set.


multi-objective optimization aspiration set preferences 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Günter Rudolph
    • 1
    Email author
  • Oliver Schütze
    • 2
  • Christian Grimme
    • 3
  • Heike Trautmann
    • 3
  1. 1.Department of Computer ScienceTU Dortmund UniversityDortmundGermany
  2. 2.Department of Computer ScienceCINVESTAVMexico CityMexico
  3. 3.Department of Information SystemsUniversity of MünsterMünsterGermany

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