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The Neural Networks Ensembles solving Job Shop Schedule Problem based on evolutionary programming

机译:基于进化规划的神经网络集合解决了作业商店日程问题

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A evolutionary programming is proposed in this paper to automatically design neural netnorks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process. In addition, the architecture of each NN in the ensemble and the size of the ensemble need not to be predefined. The Neural Networks Ensembles based on evolutionary programming is designed in order to solve Job Shop Schedule Problem. The simulation results show that the proposed method in this paper is valid.
机译:本文提出了一种进化的编程,以自动设计神经NetNorks(NNS)合奏。基于负相关学习,集合中的不同个体NN可以学习细分任务,从而更有效地解决它。与此同时,不同的单个NNS始终在进化过程中找到最佳的协作连接。另外,Ensemble中每个NN的架构和集合的大小不需要预定义。设计了基于进化编程的神经网络集合,以解决工作店进度问题。仿真结果表明,本文提出的方法有效。

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