Application genetic and simulated annealing algorithm for optimization of neural network structure
Guoli Zhang · Computer Engineering and Applications Journal · 2007
A conventional neural network often optimizes the weights through invariable network structure which has limited the extensive use of the neural network.The crossover operator based on direction and mutation are introduced.This paper puts forward genetic and simulated annealing algorithm to train the neural networks,combining the merits of genetic algorithm and that of simulated annealing algorithm,which makes weights and structure of artificial neural networks be optimized together.The result shows that the neural network optimized by the presented algorithm has the advantages of quicker convergence rate and higher precision compared with genetic algorithm and simulated annealing algorithm,and that the processing ability of networks is raised.