An Improved Genetic Algorithm for Job-shop Scheduling
XU Lian-xiang · Mechanical Management and Development · 2009
The genetic algorihm with search area adaptation(GSA)can adapting to the structure of solution space and controlling the tradeoff balance between global and local searches even if we do not adjust the parameters of the genetic algorithm(GA)(such as crossover and mutation rates).But GSA needs the crossover operator that can control characteristic inheritance ratio.In this paper,we propose the modified genetic algorithm with search area adaptation(mGSA) for solving the Job-shop scheduling problem(JSP).Unlike GSA,the method does not need such a crossover operator.We conduct numerical experiments of bechmark problems,the result showes that this method has better performance.