In factory, it is an important problem for scheduling rapidly, and genetic algorithm is a popular method to solve the NP-hard scheduling problem. In tradition, using genetic algorithm to solve the problem needs a long time, but ''time'' is the most important problem in scheduling. This research intends to how to use the character of genetic algorithm to escape the trap in local solution, and exclude the long time of evolution from the genetic algorithm, then get the better solution in short time. In this research, we develop a combination of using crossover, and mutation rate, to derive the solution fast and better.
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