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Hybrid Supervised Learning in MLP using Real-coded GA and Back-propagation

by P. P. Sarangi, B. S. P. Mishra, B. Majhi, S. Dehuri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 21
Year of Publication: 2013
Authors: P. P. Sarangi, B. S. P. Mishra, B. Majhi, S. Dehuri
10.5120/10222-5006

P. P. Sarangi, B. S. P. Mishra, B. Majhi, S. Dehuri . Hybrid Supervised Learning in MLP using Real-coded GA and Back-propagation. International Journal of Computer Applications. 62, 21 ( January 2013), 32-39. DOI=10.5120/10222-5006

@article{ 10.5120/10222-5006,
author = { P. P. Sarangi, B. S. P. Mishra, B. Majhi, S. Dehuri },
title = { Hybrid Supervised Learning in MLP using Real-coded GA and Back-propagation },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 21 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number21/10222-5006/ },
doi = { 10.5120/10222-5006 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:12:31.338729+05:30
%A P. P. Sarangi
%A B. S. P. Mishra
%A B. Majhi
%A S. Dehuri
%T Hybrid Supervised Learning in MLP using Real-coded GA and Back-propagation
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 21
%P 32-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper addresses a classification task of pattern recognition by combining effectiveness of evolutionary and gradient descent techniques. We are proposing a hybrid supervised learning approach using real-coded GA and back-propagation to optimize the connection weights of multilayer perceptron. The following learning algorithm overcomes the problems and drawbacks of individual technique by introducing global and local adaptation strategies. The behavior of the proposed algorithm is observed by the experimental results on a couple of popular benchmark datasets. The results of our algorithm are compared with training algorithms based on conventional back-propagation and real-coded genetic algorithm. Finally we realize that proposed hybrid learning algorithm outperforms back-propagation and real-coded genetic algorithm based training the multilayer perceptron.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithms Multi-layer perceptron Gradient descent Generalization

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