The second largest eigenvalue upper bound for the canonical genetic algorithms

Research output: Contribution to journalArticle

Abstract

We provide an analysis of a Markov chain model that explains the convergence properties of canonical genetic algorithms with proportional selection, single point crossover and bit mutation with a mutation rate between 0 and 1. Specifically, we provide a second largest eigenvalue upper bound for canonical genetic algorithms (CGA). We show that, when mutation rate for CGA is 0.5, the second largest eigenvalue for the CGA is zero and the CGA converges to a stationary distribution in the first step after the initial random population initialization.

Original languageEnglish (US)
Pages (from-to)193-198
Number of pages6
JournalInternational Journal of Knowledge-Based and Intelligent Engineering Systems
Volume11
Issue number3
DOIs
StatePublished - Jan 1 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence

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