Convergence time for the linkage learning genetic algorithm

Ying-Ping Chen*, David E. Goldberg

*Corresponding author for this work

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.

Original languageEnglish
Pages (from-to)279-302
Number of pages24
JournalEvolutionary Computation
Volume13
Issue number3
DOIs
StatePublished - 1 Sep 2005

Keywords

  • Convergence time
  • Genetic algorithms
  • Genetic linkage
  • Linkage learning
  • Linkage learning genetic algorithm
  • Sequential behavior
  • Tightness time

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