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Proceedings Paper

Optimizing feed-forward neural networks using cascaded genetic algorithm
Author(s): Lin-xia Zhou; Ming Li; Xiaoqin Yang
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Paper Abstract

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.

Paper Details

Date Published: 25 September 2003
PDF: 4 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538873
Show Author Affiliations
Lin-xia Zhou, Nanchang Institute of Aeronautical Technology (China)
Ming Li, Nanchang Institute of Aeronautical Technology (China)
Xiaoqin Yang, Nanchang Institute of Aeronautical Technology (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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