On convergence rate of a class of genetic algorithms

Liang Ming*, Yuping Wang, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

18 Citations (Scopus)


Studying convergence rate of a genetic algorithm is a very important but a nontrivial task. The more accurate estimation of convergence rate for genetic algorithms can be used to design the more efficient control parameters of algorithms, and to point out the correct direction to improve the algorithms. Moreover, one good measure of convergence rate can be used to judge the efficiency of different algorithms. The existing results about the convergence rate for genetic algorithms can be classified into two types. One type is based on Doeblin condition in which some parameters should be estimated. The other type needs to estimate the eigenvalues of the state transition matrix. However, these parameters are difficult to estimate. In this paper, we first formulate a model for a class Of genetic algorithms, and then analyze the convergence rate of such an algorithm in a different way. It shows that their convergence rate is linear based on property of Markov chain. Copyright - World Automation congress (WAC) 2006.

Original languageEnglish
Title of host publication2006 World Automation Congress, WAC'06
PublisherIEEE Computer Society
ISBN (Print)1889335339, 9781889335339
Publication statusPublished - 2006
Event2006 World Automation Congress, WAC'06 - Budapest, Hungary
Duration: 24 Jun 200626 Jun 2006

Publication series

Name2006 World Automation Congress, WAC'06


Conference2006 World Automation Congress, WAC'06

Scopus Subject Areas

  • Control and Systems Engineering

User-Defined Keywords

  • Convergence rate
  • Genetic algorithms
  • Markov chain


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