Pattern classification with genetic algorithms

Bandyopadhyay, S. ; Murthy, C. A. ; Pal, S. K. (1995) Pattern classification with genetic algorithms Pattern Recognition Letters, 16 (8). pp. 801-808. ISSN 0167-8655

PDF - Publisher Version

Official URL:

Related URL:


A method is proposed for finding decision boundaries, approximated by piecewise linear segments, for the classification of patterns in R2, using an elitist model of a genetic algorithm. It involves the generation and placement of a set of lines (represented by strings) in the feature space that yields minimum misclassification. The effectiveness of the algorithm is demonstrated, for different parameter values, on both artificial data and speech data having non-linear class boundaries. Its comparison with the k-NN classifier is also made.

Item Type:Article
Source:Copyright of this article belongs to International Association for Pattern Recognition.
Keywords:Elitist Model; Genetic Operators; Line Fitting; Pattern Recognition
ID Code:26065
Deposited On:06 Dec 2010 13:09
Last Modified:17 May 2016 09:25

Repository Staff Only: item control page