Optimizing Feed-forward Neural Networks Using Cascaded Genetic Algorithm

摘要

A novel method of optimizing feed-forward neural networks using cascaded genetic algorithm is proposed in this paper. It adopts a hybrid encoding method, which architectures and connection weights vector of neural networks are encoded into binary code and real-value code respectively. The proposed optimizing method includes two cascaded evolutionary procedures in which the first mainly plays the role of fast search in constrained area and the second extends global exploration ability. The proposed method has represented a particular compromise between exploitation and exploration of searching optimized neural networks and enhanced the global search ability while using less computation. The experimental results have shown its good performance.