An Improved Adaptive Genetic Algorithm On Job-Shop Scheduling

With economic of market development, many sequences and small quantity become the focal point of the market which manufactory racing to control. In this way, manufactories should be asked to range sequences rationally, take advantage of resource, shorten time limit for a project and reduce cost of producing. So people paid attention to the JSP more and more.JSP belonging to NP-hard problem, is the hardest solving problem in classic optimization problem. GA is applied in the optimization of JSP widely, because it has characteristics that it can be used generally and the algorithm is simple. It's main advantange is that it has nothing to do with echelon information during solving the problem. It can get the best result only using three operators of choosing, crossover and mutation.Although many studies and research show that GA is a more better algorithm, but in the practical apply it has the problem of premature convergence. In another word, during the evolution few individuals's fitness are more bigger than others, then during few reproduction, these individuals can occupy the entire group. So the process is over ahead of schedule.In order to solve the disadvantage of premature convergence. This paper presents an improved genetic algorithm which can enhance global searching ability and quicken convergent speed.