Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2007

Abstract

In this paper, we investigate the application of lateral symmetry to supervised learning using genetic algorithms. The hypothesis is motivated by the presence of symmetry in the animal brain and by research results showing approximately equal task division between the two hemispheres of the brain. In this paper, each training pattern is considered a task. By applying the concept of lateral symmetry, we use global training (a typically right brained activity) to learn half the tasks and local training (a left brained activity) to learn the rest of the tasks. We verified the use of this Percentage-based Pattern (PHP) training approach using various comprehensive programs and applied this approach to genetic algorithm based curve fitting problems. The results in both cases were encouraging. The PHP-based hybrid algorithms resulted in significant reduction in the testing error as well as in the training time. The PHP algorithm is therefore concluded to be an approach towards more controlled learning algorithm in a field dominated by blind search methods.

Keywords

genetic algorithm, training pattern, hybrid training, pattern learning, training parameters, supervised learning

Discipline

Databases and Information Systems | Theory and Algorithms

Publication

Journal of Intelligent Systems

Volume

16

Issue

3

First Page

241

Last Page

273

ISSN

0334-1860

Identifier

10.1515/JISYS.2007.16.3.241

Publisher

De Gruyter

Additional URL

https://doi.org/10.1515/JISYS.2007.16.3.241

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