PolyU IR
 

PolyU Institutional Repository >
Electronic and Information Engineering >
EIE Conference Papers & Presentations >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1376

Title: Real-coded genetic algorithm with average-bound crossover and wavelet mutation for network parameters learning
Authors: Ling, S. H.
Leung, Frank H. F.
Subjects: Benchmarking
Genetic algorithms
Genetic engineering
Learning systems
Neurology
Parameter estimation
Wavelet transforms
Issue Date: 2005
Publisher: IEEE
Citation: 2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 1325-1330.
Abstract: This paper presents the learning of neural network parameters using a real-coded genetic algorithm (RCGA) with proposed crossover and mutation. They are called the average-bound crossover (AveBXover) and wavelet mutation (WM). By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. An application example on an associative memory neural network is used to show the learning performance brought by the proposed RCGA.
Rights: © 2005 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.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Conference Paper
URI: http://hdl.handle.net/10397/1376
ISBN: 0-7803-9048-2
Appears in Collections:EIE Conference Papers & Presentations

Files in This Item:

File Description SizeFormat
Real-coded genetic algorithm_05.pdf1.03 MBAdobe PDFView/Open



Facebook Facebook del.icio.us del.icio.us LinkedIn LinkedIn


All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
No item in the PolyU IR may be reproduced for commercial or resale purposes.

 

© Pao Yue-kong Library, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Powered by DSpace (Version 1.5.2)  © MIT and HP
Feedback | Privacy Policy Statement | Copyright & Restrictions - Feedback