Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/292
PIRA download icon_1.1View/Download Full Text
Title: Adding learning to cellular genetic algorithms for training recurrent neural networks
Authors: Ku, KWC
Mak, MW 
Siu, WC 
Issue Date: Mar-1999
Source: IEEE transactions on neural networks, Mar. 1999, v. 10, no. 2, p. 239-252
Abstract: This paper proposes a hybrid optimization algorithm which combines the efforts of local search (individual learning) and cellular genetic algorithms (GA's) for training recurrent neural networks (RNN's). Each weight of an RNN is encoded as a floating point number, and a concatenation of the numbers forms a chromosome. Reproduction takes place locally in a square grid with each grid point representing a chromosome. Two approaches, Lamarckian and Baldwinian mechanisms, for combining cellular GA's and learning have been compared. Different hill-climbing algorithms are incorporated into the cellular GA's as learning methods. These include the real-time recurrent learning (RTRL) and its simplified versions, and the delta rule. The RTRL algorithm has been successively simplified by freezing some of the weights to form simplified versions. The delta rule, which is the simplest form of learning, has been implemented by considering the RNN's as feedforward networks during learning. The hybrid algorithms are used to train the RNN's to solve a long-term dependency problem. The results show that Baldwinian learning is inefficient in assisting the cellular GA. It is conjectured that the more difficult it is for genetic operations to produce the genotypic changes that match the phenotypic changes due to learning, the poorer is the convergence of Baldwinian learning. Most of the combinations using the Lamarckian mechanism show an improvement in reducing the number of generations required for an optimum network; however, only a few can reduce the actual time taken. Embedding the delta rule in the cellular GA's has been found to be the fastest method. It is also concluded that learning should not be too extensive if the hybrid algorithm is to be benefit from learning.
Keywords: Baldwin effect
Genetic algorithms
Lamarckian learning
Real-time recurrent learning
Recurrent neural networks
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE transactions on neural networks 
ISSN: 1045-9227
DOI: 10.1109/72.750546
Rights: © 1999 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.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
neural-networks_99.pdf297.8 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

124
Last Week
1
Last month
Citations as of Mar 24, 2024

Downloads

182
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

30
Last Week
0
Last month
0
Citations as of Mar 28, 2024

WEB OF SCIENCETM
Citations

30
Last Week
0
Last month
0
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.