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中文摘要：
针对Job—Shop调度问题，提出了一种双染色体的遗传算法编码新方法，采用对染色体的分离交叉重组操作生成更多的优秀个体，设计了多种群、遗传参数自适应词整来提高种群的多样性。使用优势档案群保存当代最优Pareto解。最后给出仿真结果，与经典的遗传算法求得的结果比较，证明了该算法的有效性和先进性。
英文摘要：
Gonsidering Job- Shop scheduling problems, introduce a double- chromosome genetic coding algorithm , in which cross, reorganization and separation operations are used to generate more outstanding individuals, through multi - population and self- adjustment of parameters, the diversity of the population is increased. Archivcd is used to store temporary Pareto optimal solution. Finally, the simulation result is compared with the result obtained by classical genetic algorithm to prove the effectiveness and advantage of the algorithm.
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