Performance Optimization of Adaptive Resonance Neural Networks Using Genetic Algorithms

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...

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Bibliographic Details
Main Authors: Al-Natsheh, H.T., Eldos, T.M.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Artificial neural networks
Biological cells
Computational intelligence
Genetic algorithms
Genetic engineering
Neural networks
Optimization
Resonance
Subspace constraints
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Summary: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
DOI:10.1109/FOCI.2007.372160