A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem

The genetic algorithm with search area adaptation (GSA) has a capacity for 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/or mutation rates. But, GSA needs the crossover operator that has ability for characteristic inheritance ratio control. In this paper, we propose the modified genetic algorithm with search area adaptation (mGSA) for solving the Job-shop scheduling problem. Unlike GSA, our proposed method does not need such a crossover operator. To show the effectiveness of the proposed method, we conduct numerical experiments by using two benchmark problems. It is shown that this method has better performance than existing GAs.