Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network

DOI

10.2991/ijcis.2010.3.6.3How to use a DOI?

Keywords

Neural evolution, selective cloning, performance enhancement

Abstract

The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. The Iris data problem is used as a bench-mark to compare the selective cloning technique with the conventional GA and the back-propagation algorithm. For comparative analysis, same neural network architecture is used for both the back propagation and the genetic algorithms. The selective cloning approach is based on the schema theorem. By using selective cloning, it has been shown that GA is 27.78% more efficient than the conventional GA and 83.33% more efficient than the back propagation approach. The results of selective cloning on other data sets are also discussed.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Devinder Kaur
AU  - Praneeth Nelapati
PY  - 2010
DA  - 2010/12/01
TI  - Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network
JO  - International Journal of Computational Intelligence Systems
SP  - 723
EP  - 732
VL  - 3
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2010.3.6.3
DO  - 10.2991/ijcis.2010.3.6.3
ID  - Kaur2010
ER  -