Integration of supervised ART-based neural networks with a hybrid genetic algorithm

Tan, Shing Chiang and Lim, Chee Peng 2011, Integration of supervised ART-based neural networks with a hybrid genetic algorithm, Soft computing, vol. 15, no. 2, pp. 205-219.

Attached Files
Name Description MIMEType Size Downloads

Title Integration of supervised ART-based neural networks with a hybrid genetic algorithm
Author(s) Tan, Shing Chiang
Lim, Chee Peng
Journal name Soft computing
Volume number 15
Issue number 2
Start page 205
End page 219
Total pages 15
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2011-02
ISSN 1432-7643
1433-7479
Keyword(s) dynamic decay adjustment algorithm
evolutionary artificial neural network
fuzzy ARTMAP
hybrid genetic algorithm
pattern classification
Summary In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048776

Document type: Journal Article
Collection: Institute for Frontier Materials
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 2 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 20 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Wed, 26 Sep 2012, 09:26:23 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.