Drift conditions for estimating the first hitting times of evolutionary algorithms

Yu Chen, Xiufen Zou, Jun He

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)

Abstract

For the global optimization problems with continuous variables, evolutionary algorithms (EAs) are often used to find the approximate solutions. The number of generations for an EA to find the approximate solutions, called the first hitting time, is an important index to measure the performance of the EA. However, calculating the first hitting time is still difficult in theory. This paper proposes some new drift conditions that are used to estimate the upper bound of the first hitting times of EAs for finding the approximate solutions. Two case studies are given to show how to apply these conditions to estimate the first hitting times.
Original languageEnglish
Pages (from-to)37-50
Number of pages13
JournalInternational Journal of Computer Mathematics
Volume88
Issue number1
Early online date02 Dec 2010
DOIs
Publication statusPublished - 01 Jan 2011

Keywords

  • complexity theory
  • evolutionary algorithm
  • global optimization
  • approximate solutions
  • first hitting time

Fingerprint

Dive into the research topics of 'Drift conditions for estimating the first hitting times of evolutionary algorithms'. Together they form a unique fingerprint.

Cite this