The optimal architecture of a deep feedforward neural network (DFNN) is essential for its better accuracy and faster convergence. Also, the training of DFNN becomes tedious as the depth of the network increases. The DFNN can be tweaked using several parameters, such as the number of hidden layers, the number of hidden neurons at each hidden layer, and the number of connections between layers. The optimal architecture of DFNN is usually set using a trial-and-error process, which is an exponential combinatorial problem and a tedious task. To address this problem, we need an algorithm that can automatically design an optimal architecture with improved generalization ability. This work aims to propose a new methodology that can simultaneously optimize the number of hidden layers and their respective neurons for DFNN. This work combines the advantages of Tabu search and Gradient descent with a momentum backpropagation training algorithm. The proposed approach has been tested on four different classification benchmark datasets, which show better generalization ability of the optimized networks.

中文翻译：

---

优化深度前馈神经网络体系结构：基于禁忌搜索的方法

深度前馈神经网络（DFNN）的最佳架构对其更好的准确性和更快的收敛性至关重要。而且，随着网络深度的增加，DFNN的训练变得乏味。可以使用多个参数来调整DFNN，例如隐藏层的数量，每个隐藏层的隐藏神经元的数量以及层之间的连接数。DFNN的最佳体系结构通常使用反复试验过程来设置，这是一个指数组合问题和繁琐的任务。为了解决这个问题，我们需要一种能够自动设计具有改进泛化能力的最佳架构的算法。这项工作旨在提出一种可以同时优化DFNN的隐藏层及其各自神经元数量的新方法。这项工作结合了禁忌搜索和梯度下降的优势以及动量反向传播训练算法。该方法已在四个不同的分类基准数据集上进行了测试，这些数据集显示了优化网络的更好泛化能力。