Optimizing Weights of Artificial Neural Networks using Genetic Algorithms

Artificial Neural Networks have a number of properties which make them psuitable to solve complex pattern classification problems. Their applications to some real world problems has been adopted by the lack of a training algorithm. This algorithms finds a nearly globally optimal set of weights in a relatively short time. Back propagation is one of the training algorithm of the Artificial neural network. However, training the neural networks using backpropagation algorithm may cause two main drawbacks: trapping into local minima and converging slowly. In view of these limitations of back-propagation neural networks, global search technique such as Genetic algorithm have been presented to overcome these shortcomings. Genetic algorithms are a class of optimization procedures which are good at exploring a large and complex space in an intelligent way. It finds values close to the global optimum. Hence, they are well suited to the problem of training and optimize weights of Artificial Neural Networks. In this paper the use of Genetic algorithms to optimize weights of Artificial Neural Networks is shown.