Training a Feed-Forward Neural Network Using Cuckoo Search

Abstract

Cuckoo Search (CS) is a nature-inspired and metaheuristic algorithm which is based on a brood reproductive strategy of cuckoo birds to increase their population. This algorithm mainly serves to determine the maximum or minimum value of a particular problem which is known as the objective function. CS has reportedly outperformed other nature-inspired algorithms in terms of computational efficiency and the speed of convergence to reach an optimal solution. This chapter aims at exploring the application of CS to determine the parameters of Artificial Neural Networks (ANN). The inherent problem with traditional training of ANNs using backpropagation is that the learning process cannot guarantee a global minimum solution and has a tendency of getting trapped in local minima. The working of such ANN models is restricted to a differentiable neuron transfer function. The CS algorithm has been observed to provide a solution without the use of derivates to optimize such convoluted problems. The usage of ANNs across a wide range of problems including classification tasks, image processing, signal processing, etc. justifies the application of CS to the backpropagation procedure of ANNs to achieve a faster rate of convergence and avoid the local minima problem. This chapter also presents discussions and results on how ANNs optimized with variants of CS perform when applied to the detection of chronic kidney disease, modelling of operating photovoltaic module temperature and forest type classification.