A Modified Differential Evolution Algorithm for Optimization Neural Network
Authors
Ning Guiying1, Zhou Yongquan
1College of Mathematics and Computer Science, Guangxi University for Nationalities
Corresponding Author
Ning Guiying
Available Online October 2007.
- DOI
- 10.2991/iske.2007.30How to use a DOI?
- Keywords
- evolution strategies; neural network; 1/2 rule; evolution algorithm; differential evolution
- Abstract
A Modified Differential Evolution (MDE) was proposed, which based on the basic Differential Evolution (DE) algorithm principle and implementing framework of DE. Optimizing the initial individuals with the 1/2 rule, and then introducing the reorganization of Evolution Strategies during the period of mutation procedures. The MDE was used to optimize the weights of the feed-forward multilayer neural network, and compared with the basic DE and BP algorithm with momentum term. Finally, the numerical simulation results show that this method has good quality of high-speed global convergence and effectively improves the precision and convergence speed for feed-forward multilayer neural network, it has been proven the effectively and feasibility.
- Copyright
- © 2007, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ning Guiying AU - Zhou Yongquan PY - 2007/10 DA - 2007/10 TI - A Modified Differential Evolution Algorithm for Optimization Neural Network BT - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007) PB - Atlantis Press SP - 173 EP - 177 SN - 1951-6851 UR - https://doi.org/10.2991/iske.2007.30 DO - 10.2991/iske.2007.30 ID - Guiying2007/10 ER -