Adaptive genetic algorithms for the Job-Shop Scheduling Problems

In order to solve the feeble adaptability and the imbalance between random search and local search in the job-shop scheduling problem, a new adaptive genetic algorithm (AGA) was presented in this paper. The superiority of this algorithm was the adaptation achieved by adjusting the crossover rate and mutation rate. At the same time, the search property has been balanced by restricting crossover and mutation. To insure the best chromosome pass to the next generation, we immediately reserved the best chromosome. Operation-based representation was adopted. Therefore, work piece position-based crossover and search region-based mutation was applied in this paper. The developed algorithm had been tested by benchmark problems. Computational results show this adaptive genetic algorithm (AGA) has an effective search behavior. This can get away from local optimal and avoid premature convergence. Also the convergence speed increases.