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Indicator-Based Selection in Multiobjective Search

  • Eckart Zitzler
  • Simon Künzli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSGA-II and SPEA2, with respect to different performance measures.

Keywords

Knapsack Problem Decision Vector Population Member Optimization Goal Multiobjective Evolutionary Algorithm 
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 2004

Authors and Affiliations

  • Eckart Zitzler
    • 1
  • Simon Künzli
    • 1
  1. 1.Computer Engineering and Networks Laboratory (TIK)Swiss Federal Institute of Technology ZurichZürichSwitzerland

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