Authors : D. Shanthi , G. Sahoo and N. Saravanan
Abstract: In Artificial Neural Network (ANN), the selection of connection weights is a key issue. The usual weights’ randomization methods are used to initialize the network weights before training. The main aim of weights randomization techniques is to avoid sigmoid saturation problems that cause slow training. There are different weights randomization methods such as Manual, Automatic, optimized for uniform distribution of networks, optimized for Gaussian distribution of network input and Random seed available. In this study, we proposed a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
D. Shanthi , G. Sahoo and N. Saravanan , 2009. Evolving Connection Weights of Artificial Neural Networks Using Genetic Algorithm with Application to the Prediction of Stroke Disease. International Journal of Soft Computing, 4: 95-102.