Evolutionary Algorithm for Optimal Connection Weights in Artificial Neural Networks

A neural network may be considered as an adaptive system that progressively self-organizes inorder to approximate the solution, making the problem solver free from the need to accuratelyand unambiguously specify the steps towards the solution. Moreover, Evolutionary computationcan be integrated with artificial Neural Network to increase the performance at various levels; inresult such neural network is called Evolutionary ANN. In this paper very important issue of neuralnetwork namely adjustment of connection weights for learning presented by Genetic algorithmover feed forward architecture. To see the performance of developed solution comparison hasgiven with respect to well established method of learning called gradient decent method. Abenchmark problem of classification, XOR, has taken to justify the experiment. Presented methodis not only having very probability to achieve the global minima but also having very fastconvergence.