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A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem

LIXiang-jun、WANGShu-zhen、XUGuo-hua

2004国际设备工程与管理：英文版Engineering被引 0

摘要

The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.

引用本文（GB/T 7714）