Training of Multi-Branch Neural Networks using RasID-GA
Dongkyu Sohn, Shingo Mabu, Kaoru Shimada, Kotaro Hirasawa, Jinglu Hu · 2007
This paper applies a Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) to neural network training. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for optimization. Neural networks are widely used in pattern recognition, system modeling, prediction and other areas. Although most neural network training uses gradient based schemes such as wellknown back-propagation (BP), but sometimes BP is easily dropped into local minima. In this paper, we train multi-branch neural networks using RasID-GA with constraint coefficient C by which the feasible solution space is controlled. In addition, we use Mackey-Glass time prediction to test a generalization ability of the proposed method.