Genetic operators design using division algorithm in the integer solution space

Li Guiting, Wang Bingtuan, Li Aihua

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Genetic algorithm (GA) is a well known algorithm applied to a wide variety of optimization problems [4]. It combines selection, crossover, and mutation operators in order to find the best solution to a problem. The standard GA operates on chromosomes represented by binary code strings [1, 2]. This paper designs alternative operators in the GA process. The new operations reduce the binary decoding process of chromosomes when performing the computation. Variations of solutions with the implemented operations on chromosomes are studied. Computational examples show that the new methods save the computer time and enhance the efficiency when compared to the standard GA.

Original languageEnglish
Title of host publicationProceedings of the 17th IASTED International Conference on Modelling and Simulation
Pages286-290
Number of pages5
StatePublished - 27 Nov 2006
Event17th IASTED International Conference on Modelling and Simulation - Montreal, QC, Canada
Duration: 24 May 200626 May 2006

Publication series

NameProceedings of the IASTED International Conference on Modelling and Simulation
Volume2006
ISSN (Print)1021-8181

Other

Other17th IASTED International Conference on Modelling and Simulation
CountryCanada
CityMontreal, QC
Period24/05/0626/05/06

Keywords

  • Crossover
  • Genetic operator/algorithm
  • Mutation
  • Selection

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