On Using Populations of Sets in Multiobjective Optimization

  • Johannes Bader
  • Dimo Brockhoff
  • Samuel Welten
  • Eckart Zitzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5467)


Most existing evolutionary approaches to multiobjective optimization aim at finding an appropriate set of compromise solutions, ideally a subset of the Pareto-optimal set. That means they are solving a set problem where the search space consists of all possible solution sets. Taking this perspective, multiobjective evolutionary algorithms can be regarded as hill-climbers on solution sets: the population is one element of the set search space and selection as well as variation implement a specific type of set mutation operator. Therefore, one may ask whether a ‘real’ evolutionary algorithm on solution sets can have advantages over the classical single-population approach. This paper investigates this issue; it presents a multi-population multiobjective optimization framework and demonstrates its usefulness on several test problems and a sensor network application.


Evolutionary Algorithm Multiobjective Optimization Optimizer Variant Mating Selection Environmental Selection 
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 2009

Authors and Affiliations

  • Johannes Bader
    • 1
  • Dimo Brockhoff
    • 1
  • Samuel Welten
    • 1
  • Eckart Zitzler
    • 1
  1. 1.Computer Engineering and Networks LabETH ZurichZurichSwitzerland

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