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


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
Issue number3
StatePublished - 1 Sep 2005


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

Fingerprint Dive into the research topics of 'Convergence time for the linkage learning genetic algorithm'. Together they form a unique fingerprint.

  • Cite this