| Article ID: | iaor2005523 |
| Country: | China |
| Volume: | 30 |
| Issue: | 2 |
| Start Page Number: | 267 |
| End Page Number: | 270 |
| Publication Date: | Apr 2003 |
| Journal: | Journal of Xidian University |
| Authors: | Wang Shuzhen, Xu Dian |
| Keywords: | genetic algorithms, job shop |
The standard genetic algorithm has limitations of low convergence rate and premature convergence in solving job-shop scheduling problem, and some some improved algorithms available only solve one of those limitations. This paper presents a Grafted Genetic Algorithm inspired by grafting in botany. The improved algorithm accelerates convergence rate greatly and also increases the ability to fight premature stopping by introducing grafted population and crossover probability matrix.