基于自适应遗传算法的流水车间作业调度

流水车间调度问题是NP完全问题。提出一种新的自适应遗传算法，采用初始种群复合化、适应度相同个体的筛选策略、改进自适应交叉变异概率等方法提高算法性能。通过仿真比较，从最优解出现的代数、最优解的相对误差以及随机若干次试验对算法的影响3个方面证明该算法的优越性。

Flow Job Shop Scheduling Based on Self-adaptive Genetic Algorithm

Flow shop scheduling problem is a Non-Polynomial complete problem. This paper presents a new self-adaptive genetic algorithm. Improved methods including compounded initial population, filter stratagem for the individual with same fitness value and improved self-adaptive across aberrance probability are adopted to improve the algorithm performance. The simulation results show that the improved algorithm has high performance in terms of the best result, the relative error of the best results and robustness.