Genetic and tabu search algorithm for the optimization of neural network structure

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 Tabu search mutation operator was introduced. This paper put forward Genetic and Tabu search algorithm to train the neural networks, combining the merits of genetic algorithm and that of Tabu search algorithm, which makes weights and structure of artificial neural networks be optimized together. The result shows that the neural network optimized by using the presented algorithm has the advantages of quicker convergence rate and higher precision, compared with genetic algorithm and Tabu search algorithm, and that the processing ability of networks is also raised.