Evolving Artificial Neural Networks Using GA and Momentum
Neural network learning methods provide a robust approach to approximating real-valued, discrete-valued and vector-valued target functions. Artificial neural networks are among the most effective learning methods currently known for certain types of problems. But BP training algorithm is based on the error gradient descent mechanism that the weight inevitably fall into the local minimum points. Genetic Algorithms (GAs) is good at global searching, and search for precision appears to be partial capacity inadequate. So, in this paper, the genetic operators were carefully designed to optimize the neural network, avoiding premature convergence and permutation problems. And with the momentum to solve the slow convergence problem of BP algorithm. To evaluate the performance of the genetic algorithm-based neural network, BP neural network was also involved for a comparison purpose. The results indicated that Gas and with momentum were successful in evolving ANNs.
BP neural network genetic algorithms momentum
Huawang Shi
School of Civil Engineering Hebei University of Engineering Handan, P.R.China
国际会议
Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)
南昌
英文
475-478
2009-05-22（万方平台首次上网日期，不代表论文的发表时间）