Genetic Algorithm based Weights Optimization of Artificial Neural Network
To develop an accurate process model using Artificial Neural Network (ANN), the learning process or training and validation are among the important steps. In the training process, a set of input-output patterns is repeated to the ANN. From that, weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. Through these activities, the ANN learns the correct input-output response behaviour. For validation, the ANN is subjected to input patterns unseen during training, and introduces adjustment to make the system more reliable and robust. It is also used to determine the stopping point before over fitting occurs. A typical fitting criterion may be introduced to emphasis the model validity. Such criterion may be mean square error (MSE), sum square error (SSE) which is calculated between the target and the network output. Research on using genetic algorithms for neural networks learning is increasing. Paper presented, genetic algorithm used for the weights optimization on a pre-specified neural network applied to decide the value of hello interval of the Ad hoc On Demand Distance Vector (AODV) routing protocol of the Mobile Ad-Hoc Network (MANET).