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Title: Tuning of the structure and parameters of a neural network using an improved genetic algorithm
Authors: Leung, FHF 
Lam, HK
Ling, SH
Tam, PKS
Keywords: Genetic algorithm (GA)
Neural networks
Parameter learning
Structure learning
Issue Date: Jan-2003
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks, Jan. 2003, v. 14, no. 1, p. 79-88 How to cite?
Journal: IEEE transactions on neural networks 
Abstract: This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It will also be shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.
ISSN: 1045-9227
DOI: 10.1109/IECON.2001.976448
Rights: © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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