Multilayer perceptron training using an evolutionary algorithm

It is shown through a considerably large literature review that combinations of Artificial Neural Networks (ANNs) and Evolutionary Algorithms (EAs) can lead to significantly better intelligent systems than relying on ANNs or EAs alone. Evolution can be introduced into ANNs at many different levels. This paper focuses on the evolution of connection weights, which provides a global approach to connection weight training especially when gradient information of the error function is difficult or costly obtained. Due to the simplicity and generality of the evolution and the fact that gradient-based training algorithm often have to be run multiple times in order to avoid being trapped in a poor local optimum, the evolutionary approach is quite competitive. This paper takes a step in that direction by introducing an EA for Multi-Layer Perceptron (MLP) learning, called Perceptron Learning using Genetic algorithm (PLG), that gets results comparably better than BackPropagation (BP).