Performance optimization of adaptive resonance neural networks using genetic algorithms

被引：1
|
作者：
Al-Natsheh, Hussein T. [1 ]
Eldos, Taisir M. [1 ]
机构：
[1] Jordan Univ Sci & Technol, Dept Comp Engn, Irbid 22110, Jordan
来源：
2007 IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTATIONAL INTELLIGENCE, VOLS 1 AND 2 | 2007年
关键词：
D O I：
10.1109/FOCI.2007.372160
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：143 / +
页数：2
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