|Table of Contents|

[1] Wen Xiulan, **, Song Aiguo, Duan Jianghai, et al. Evolving Neural Networks Using an Improved Genetic Algorithm* [J]. Journal of Southeast University (English Edition), 2002, 18 (4): 367-369. [doi:10.3969/j.issn.1003-7985.2002.04.016]
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Evolving Neural Networks Using an Improved Genetic Algorithm*()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
18
Issue:
2002 4
Page:
367-369
Research Field:
Information and Communication Engineering
Publishing date:
2002-12-30

Info

Title:
Evolving Neural Networks Using an Improved Genetic Algorithm*
Author(s):
Wen Xiulan1;  2**;  Song Aiguo1;  Duan Jianghai1;  Wang Yiqing1
1Department of Instrument Science and Technology, Southeast University, Nanjing 210096, China
2Mechanical Engineering College, Inner Mongolia Polytechnic University, Huhot 010062, China
Keywords:
genetic algorithms;  neural network;  nonlinear forecasting
PACS:
TN911
DOI:
10.3969/j.issn.1003-7985.2002.04.016
Abstract:
A novel real-coded improved genetic algorithm(GA)of training feed-forward neural network is proposed to realize nonlinear system forecast. The improved GA employs a generation-alternation model based the minimal generation gap(MGP)and blend crossover operators(BLX-α). Compared with traditional GA implemented in binary number, the processing time of the improved GA is faster because coding and decoding are unnecessary. In addition, it needn’t set parameters such as the probability value of crossover and mutation by experiences. Therefore, it has the advantages of simple algorithms, strong robustness and high optimization efficiency. Then forecasting nonlinear system using feed-forward neural network is presented. Simulation shows the method is rapid and effective.

References:

[1] Lam H K, Ling S H, Leung F H F, et al. Tuning of the structure and parameters of neural network using an improved genetic algorithm[A]. In: Proc of IECON[C]. 2001.25-30.
[2] Holland J H. Adaptation in natural and artificial system[M]. Ann Arbor, MI: University of Michigan Press, 1975.
[3] Satoh H, Yamamura M, Kobayashi S. Minimal generation gap model for gas considering both exploration and exploitation[A]. In: Proc of IIZUKA[C]. 1996. 494-497.
[4] Eshelman L J, Schaffer J D. Real-coded genetic algorithms and interval-schemata[J]. Foundations of Genetic Algorithms, 1993, 2: 187-202.
[5] Ono I, Kobayashi S. A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover[A]. In: Proc 7th ICGA[C]. 1997.246-253.
[6] Gong D, Xu S, Sun X. Research on fast training algorithm for recurrent neural network[A]. In: IEEE Inter Symp on Industrial Electronics[C]. 2001.446-448.

Memo

Memo:
* The project supported by the Natural Science Foundation of Jiangsu Province(BK2001402); Dissertation Foundation of Southeast University - Nanrui Jibao Corporation.
** Born in 1966, female, associate professor.
Last Update: 2002-12-20