Drift analysis and average time complexity of evolutionary algorithms

Jun He, Xin Yao

Research output: Contribution to journalArticlepeer-review

359 Citations (Scopus)

Abstract

The computational time complexity is an important topic in the theory of evolutionary algorithms (EAs). This paper reports some new results on the average time complexity of EAs. Based on drift analysis, some useful drift conditions for deriving the time complexity of EAs are studied, including conditions under which an EA will take no more than polynomial time (in problem size) to solve a problem and conditions under which an EA will take at least exponential time (in problem size) to solve a problem. The paper first presents the general results, and then uses several problems as examples to illustrate how these general results can be applied to concrete problems in analyzing the average time complexity of EAs. While previous work only considered (1+1) EAs without any crossover, the EAs considered in this paper are fairly general, which use a finite population, crossover, mutation, and selection.
Original languageEnglish
Pages (from-to)57-85
Number of pages29
JournalArtificial Intelligence
Volume127
Issue number1
DOIs
Publication statusPublished - 15 Mar 2001

Keywords

  • evolutionary algorithms
  • time complexity
  • random sequences
  • drift analysis
  • stochastic inequalities

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