On Using Populations of Sets in Multiobjective Optimization

  • Johannes Bader
  • Dimo Brockhoff
  • Samuel Welten
  • Eckart Zitzler
Conference paper

DOI: 10.1007/978-3-642-01020-0_15

Volume 5467 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Bader J., Brockhoff D., Welten S., Zitzler E. (2009) On Using Populations of Sets in Multiobjective Optimization. In: Ehrgott M., Fonseca C.M., Gandibleux X., Hao JK., Sevaux M. (eds) Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Springer, Berlin, Heidelberg

Abstract

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.

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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