Teaching Learning Based Optimization for Neural Networks Learning Enhancement

Abstract

Evolutionary computation is a collection of algorithms based on the evolution of a population towards a solution of certain problem. These algorithms can be used successfully in many applications requiring the optimization. These algorithms have been widely used to optimize the learning mechanism of classifiers, particularly on Artificial Neural Network (ANN) Classifier. Major disadvantages of ANN classifier are due to its slow convergence and always being trapped at the local minima. To overcome this problem, TLBO (Teaching learning based optimization) has been used to determine optimal value for learning mechanism. In this study, TLBO is chosen and applied to feed forward neural network to enhance the learning process. Two programs have developed, Differential Evolution Neural Network (DENN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on a Teaching learning based optimization with neural network (TLBONN) learning using various datasets. The results have revealed that TLBONN has given quite promising results in terms of smaller errors compared to PSONN and DENN.