학술논문
Training neural networks: backpropagation vs. genetic algorithms
Document Type
Conference
Author
Source
IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222) Neural networks Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on. 4:2673-2678 vol.4 2001
Subject
Language
ISSN
1098-7576
Abstract
There are a number of problems associated with training neural networks with backpropagation algorithm. The algorithm scales exponentially with increased complexity of the problem. It is very often trapped in local minima, and is not robust to changes of network parameters such as number of hidden layer neurons and learning rate. The use of genetic algorithms is a recent trend, which is good at exploring a large and complex search space, to overcome such problems. In this paper a genetic algorithm is proposed for training feedforward neural networks and its performances is investigated. The results are analyzed and compared with those obtained by the backpropagation algorithm.