Search biases in constrained evolutionary optimization

Thomas Philip RUNARSSON, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

387 Citations (Scopus)

Abstract

A common approach to constraint handling in evolutionary optimization is to apply a penalty function to bias the search toward a feasible solution. It has been proposed that the subjective setting of various penalty parameters can be avoided using a multiobjective formulation. This paper analyzes and explains in depth why and when the multiobjective approach to constraint handling is expected to work or fail. Furthermore, an improved evolutionary algorithm based on evolution strategies and differential variation is proposed. Extensive experimental studies have been carried out. Our results reveal that the unbiased multi-objective approach to constraint handling may not be as effective as one may have assumed. © 2005 IEEE.
Original languageEnglish
Pages (from-to)233-243
Number of pages11
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume35
Issue number2
Early online date25 Apr 2005
DOIs
Publication statusPublished - May 2005
Externally publishedYes

Keywords

  • Evolution strategy
  • Multiobjective optimization
  • Nonlinear programming
  • Penalty functions

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