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Performance Optimization of Adaptive Resonance Neural Networks Using Genetic Algorithms

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2 Author(s)
Hussein T. Al-Natsheh ; Student Member, IEEE, Department of Computer Engineering, Jordan University of Science and Technology. ; Taisir M. Eldos

We present a hybrid clustering system that is based on the adaptive resonance theory 1 (ART1) artificial neural network (ANN) with a genetic algorithm (GA) optimizer, to improve the ART1 ANN settings. As a case study, we will consider text clustering. The core of our experiments will be the quality of clustering, multi-dimensional domain space of ART1 design parameters has many possible combinations of values that yield high clustering quality. These design parameters are hard to estimate manually. We proposed GA to find some of these sets. Results show better clustering and simpler quality estimator when compared with the existing techniques. We call this algorithm genetically engineered parameters ART1 or ARTgep

Published in:

Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on

Date of Conference:

1-5 April 2007