Optimizing Deep Feedforward Neural Network Architecture: A Tabu Search Based Approach

Abstract

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.