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Research of job-shop scheduling problem based on improved crossover strategy genetic algorithm

机译:基于改进交叉策略遗传算法的车间调度问题研究

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The article proposes a crossover strategy improved genetic algorithm to solve the shop scheduling problem for the traditional genetic algorithm. The algorithm uses coding method, based on the process and the introduction of the linear weighted fitness function, the greedy count sub-fitness ratio algorithm combined select operation. The improved algorithm is able to improve search efficiency, accuracy, and avoid premature shortcomings.
机译:提出了一种交叉策略改进的遗传算法,解决了传统遗传算法的车间调度问题。该算法采用编码方法,根据处理过程和线性加权适应度函数的引入,将贪婪计数子适应率算法组合选择运算。改进的算法能够提高搜索效率,准确性并避免过早的缺点。

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