An improved genetic algorithm with recurrent search for the job-shop scheduling problem

Yingjie Xing, Zhuqing Wang, Jing Sun, Wanlei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

A genetic algorithm with some improvement is proposed to avoid the local optimum for job-shop scheduling problem (JSP). There is recurrent searching process of genetic operation in the improved genetic algorithm. The improved crossover operation can shake current population from local optimum in genetic algorithm. The recurrent crossover operation and mutation operation can inherit excellent characteristics from parent chromosomes and accelerate the diversity of offspring. Both benchmark FT(6×6) and LA1(10×5) job-shop scheduling problems are used to show the effectiveness of the proposed method. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily, and it is fast in convergence, simple to be implemented.

Original languageEnglish
Title of host publicationProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages3386-3390
Number of pages5
DOIs
Publication statusPublished - 2006 Dec 1
Externally publishedYes
Event6th World Congress on Intelligent Control and Automation, WCICA 2006 - Dalian, China
Duration: 2006 Jun 212006 Jun 23

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume1

Conference

Conference6th World Congress on Intelligent Control and Automation, WCICA 2006
CountryChina
CityDalian
Period06/6/2106/6/23

Keywords

  • Crossover operation
  • Genetic algorithm
  • Job-shop
  • Recurrent search

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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