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Multi-objective optimization of a stacked neural network using an evolutionary hyper-heuristic

机译：使用进化超启发式算法的堆叠神经网络多目标优化

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The present paper deals with the development and optimization of a stacked neural network (SNN) through an evolutionary hyper-heuristic, called NSGA-II-QNSNN. The proposed hyper-heuristic is based on the NSGA-II (Non-dominated Sorting Genetic Algorithm - II) multi-objective optimization evolutionary algorithm which incorporates the Quasi-Newton (QN) optimization algorithm. QN is used for training each neural network from the stack. The final global optimal solution provided by NSGA-II-QNSNN algorithm is a Pareto optimal front. It represents all the equally good compromises that can be made between the structural complexity of the stacked neural network and its modelling performance. The set of decision variables, which led to obtaining the set of points in the Pareto optimal front, represents the optimum values for the parameters of the stacked neural network: the number of networks in the stack, the weights for every output of the composing networks, and the number of hidden neurons in each individual neural network. Each stacked neural network determined through the optimization process was trained and tested by applying it to a real world problem: the modelling of the polyacrylamide-based multicomponent hydrogels synthesis. The neural modelling established the influence of the reaction conditions on the reaction yield and the swelling degree. The results provided by NSGA-II-QNSNN were superior, not only in terms of performance, but also in terms of structural complexity, to those obtained in our previous works, where individual or aggregated neural networks were used, but the stacks were developed manually, based on successive trials.
机译：本文通过一种称为NSGA-II-QNSNN的进化超启发式方法来研究堆叠神经网络（SNN）的开发和优化。提出的超启发式算法基于NSGA-II（非主导排序遗传算法-II）多目标优化进化算法，该算法结合了拟牛顿（QN）优化算法。 QN用于训练堆栈中的每个神经网络。 NSGA-II-QNSNN算法提供的最终全局最优解是Pareto最优前沿。它代表了在堆叠神经网络的结构复杂性与其建模性能之间可以做出的所有同样好的折衷。导致获得Pareto最优前沿的点集的一组决策变量代表了堆叠神经网络参数的最优值：堆叠中网络的数量，组成网络的每个输出的权重，以及每个神经网络中隐藏神经元的数量。通过优化过程确定的每个堆叠神经网络都经过培训和测试，并将其应用于一个现实世界的问题：基于聚丙烯酰胺的多组分水凝胶合成的建模。神经模型建立了反应条件对反应收率和溶胀度的影响。 NSGA-II-QNSNN提供的结果，不仅在性能方面，而且在结构复杂性方面，都优于我们以前的使用单个或集合神经网络的结果，但是这些堆栈是手动开发的，基于连续的试验。

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