Parameter Tuning of MOEAs Using a Bilevel Optimization Approach

  • Martin AnderssonEmail author
  • Sunith Bandaru
  • Amos Ng
  • Anna Syberfeldt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9018)


The performance of an Evolutionary Algorithm (EA) can be greatly influenced by its parameters. The optimal parameter settings are also not necessarily the same across different problems. Finding the optimal set of parameters is therefore a difficult and often time-consuming task. This paper presents results of parameter tuning experiments on the NSGA-II and NSGA-III algorithms using the ZDT test problems. The aim is to gain new insights on the characteristics of the optimal parameter settings and to study if the parameters impose the same effect on both NSGA-II and NSGA-III. The experiments also aim at testing if the rule of thumb that the mutation probability should be set to one divided by the number of decision variables is a good heuristic on the ZDT problems. A comparison of the performance of NSGA-II and NSGA-III on the ZDT problems is also made.


Parameter tuning NSGA-II NSGA-III ZDT Bilevel optimization Multi-objective problems 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Martin Andersson
    • 1
    Email author
  • Sunith Bandaru
    • 1
  • Amos Ng
    • 1
  • Anna Syberfeldt
    • 1
  1. 1.Virtual Systems Research CentreUniversity of SkövdeSkövdeSweden

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