A Novel approach in Classification by Evolutionary Neural Networks

Artificial neural network is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. Neural network optimization based on three basic parameters topology, weights and the learning rate. The overfitting is a problem in NN and it produced when discordant input data with before data. We introduce optimal method for solving this problem. In this paper genetic algorithm with mutation and crossover operators by two approaches on coding solutions by optimizing the weights and network structure is encoded. Also used the simulated annealing by this idea that coordination between mutation rate in GA and Temperature in SA is suitable for grid of local optimum, Plateau and fast learning.