An Atypical Metaheuristic Approach to Recognize an Optimal Architecture of a Neural Network

The structural design of an Artificial Neural Network (ANN) greatly determines its classification and regression capabilities. Structural design involves both the count of hidden layers and the count of neurons required in each of these hidden layers. Although various optimization algorithms have proven to be good at finding the best topology for a given number of hidden layers for an ANN, there has been little work done in finding both the optimal count of hidden layers and the ideal count of neurons needed in each layer. The novelty of the proposed approach is that a bio-inspired metaheuristic namely, the Water Cycle Algorithm (WCA) is used to effectively search space of local spaces, by using the backpropagation algorithm as the underlying algorithm for parameter optimization, in order to find the optimal architecture of an ANN for a given dataset. Computational experiments have shown that such an implementation not only provides an optimized topology but also shows great accuracy as compared to other advanced algorithms used for the same purpose.