硬二值约束满足问题中动态变量排序产生一般超启发式的神经进化方法

J. C. Ortíz-Bayliss, H. Terashima-Marín, P. Ross, Jorge Iván Fuentes-Rosado, Manuel Valenzuela-Rendón
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引用次数: 4

摘要

本文介绍了一种神经进化方法,对硬二值约束满足问题的动态变量排序产生超启发式。该模型使用遗传算法来进化神经网络的结构和参数。对于遗传算法过程中的每个周期,使用反向传播方法训练新网络。当这个过程结束时,最后一个神经网络群体中训练最好的个体代表一般的超启发式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neuro-evolutionary approach to produce general hyper-heuristics for the dynamic variable ordering in hard binary constraint satisfaction problems
This paper introduces a neuro-evolutionary approach to produce hyper-heuristics for the dynamic variable ordering for hard binary constraint satisfaction problems. The model uses a GA to evolve a population of neural networks architectures and parameters. For every cycle in the GA process, the new networks are trained using backpropagation. When the process is over, the best trained individual in the last population of neural networks represents the general hyper-heuristic.
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