No Access

USING COMPETITIVE CO-EVOLUTION TO EVOLVE BETTER PATTERN RECOGNISERS

Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. W. Montréal, Québec, Canada. H3G 1M8, Canada

Search for more papers by this author

NAWWAF KHARMA

Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. W. Montréal, Québec, Canada. H3G 1M8, Canada

Search for more papers by this author

CHRISTOPHER JENSEN

Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. W. Montréal, Québec, Canada. H3G 1M8, Canada

Search for more papers by this author

HUSSEIN MOGHNIEH

Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. W. Montréal, Québec, Canada. H3G 1M8, Canada

Search for more papers by this author

, and

JIE YAO

Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. W. Montréal, Québec, Canada. H3G 1M8, Canada

Search for more papers by this author

https://doi.org/10.1142/S1469026805001441Cited by:7 (Source: Crossref)

Abstract

We present a system for the automatic synthesis of classifiers. The CellNet system for generating binary pattern classifiers is used as a base for further experimentation. As in the original CellNet software, we evolve pattern recognisers (hunters). However, in this version called CellNet Co-Ev, we also evolve the patterns (prey) in a competitive co-evolution. Patterns evolve through the application of camouflage functions, which are used to obscure the data naturally found in the database. The addition of this competitive co-evolution yields a larger and more varied database, artificially increasing the difficulty of the classification task. Application to the CEDAR database of handwritten characters shows an increase in the reliability of the evolution of recognisers, as well as in the elimination of over-fitting, relative to the original CellNet software.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

Y. Al-Ohali, M. Cheriet and C. Suen, Pattern Recogn. 36, 111 (2003). Crossref, Google Scholar
S. P. Brumbyet al., Investigation of image feature extraction by a genetic algorithm, Proc. SPIE 3812, Proc. SPIE 3812 (1999) pp. 24–31. Google Scholar
G. Fung, J. Liu and R. Lau, Feature selection in automatic signature verification based on genetic algorithms, ICONIP'96 (1996) pp. 811–815. Google Scholar
D. E. Goldberg, B. Korb and K. Deb, Complex Syst. 3(5), 493 (1989). Google Scholar
S. Gunter and H. Bunke, Electronic Letters on Computer Vision Image Analysis 3(1), 25 (2004). Crossref, Google Scholar
Z. Guo and R. Uhrig, Using genetic algorithms to select inputs for neural networks, Proc. COGANN'92 (1992) pp. 223–234. Google Scholar
N. Kharmaet al., Int. J. Pattern Recogn. Artif. Intell. 18(6), (2004). Google Scholar
M. Kudo and J. Sklansky, Kybernetika 34(4), 429 (1998). Google Scholar
M. Mitchell , An Introduction to Genetic Algorithms ( MIT Press , 1998 ) . Crossref, Google Scholar
A. Moser, A distributed vertical genetic algorithm for feature selection, ICDAR '99 (1999) pp. 1–9. Google Scholar
M. Rizki, M. Zmuda and L. Tamburino, IEEE Trans. Evol. Comput. 6(6), 594 (2002). Crossref, Google Scholar
R. Sarker, J. Kamruzzaman and C. Newton, Int. J. Comput. Intell. Appl. 3(4), 311 (2003). Link, Google Scholar
D. Shi, W. Shu and H. Liu, IEEE Int. Conf. Syst. Man Cybernet. 5, 4201 (1998). Google Scholar
W. Siedlicki and J. Sklansky, Int. J. Pattern Recogn. 2, 197 (1998). Link, Google Scholar
F. W. M. Stentiford, IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(3), 349 (1985). Crossref, Google Scholar
H. Vafaie and K. De Jong, Robust feature selection algorithms, Proc. IEEE Int. Conf. Tools Artif. Intell. (1993) pp. 356–363. Google Scholar
C. Veenman, M. Reinders and E. Backer, IEEE Trans. Image Process. 12(3), 304 (2003). Crossref, Google Scholar