Evolutionary Algorithm for Connection Weights in Artificial Neural Networks

A neural network may be considered as an adaptive system that progressively self-organizes in order to approximate the solution, making the problem solver free from the need to accurately and unambiguously specify the steps towards the solution. Moreover, Evolutionary Artificial Neural Networks (EANNs) have the ability to progressively improve their performance on a given task by executing learning. An evolutionary computation gives adaptability for connection weights using feed forward architecture. In this paper, the use of evolutionary computation for feed-forward neural network learning is discussed. To check the validation of proposed method, XOR benchmark problem has been used. The accuracy of the proposed model is more satisfactory as compared to gradient method.