Genetic optimization of GRNN for pattern recognition without feature extraction
Expert Systems with Applications: An International Journal
Technical data mining with evolutionary radial basis function classifiers
Applied Soft Computing
Genetic optimizations for radial basis function and general regression neural networks
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Immune clonal selection wavelet network based intrusion detection
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
Information Sciences: an International Journal
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One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately define values for their free parameters. This article discusses how Radial Basis Function, RBF; networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. Finally, a model is proposed which includes representation, crossover operator and multiobjective optimization criteria. Experimental results using this model are presented.