A resource-allocating network for function interpolation
Neural Computation
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An efficient MDL-based construction of RBF networks
Neural Networks
Evolving Multilayer Perceptrons
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
GANNet: A Genetic Algorithm for Optimizing Topology and Weights in Neural Network Design
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Optimising the Widths of Radial Basis Functions
SBRN '98 Proceedings of the Vth Brazilian Symposium on Neural Networks
Fast learning in networks of locally-tuned processing units
Neural Computation
Regularization in the selection of radial basis function centers
Neural Computation
Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
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Determining the parameters of a Radial Basis Function Neural Nerwork (number of neurons, and their respective centers and radii) is often done by hand, or based in methods highly dependent on initial values. In this work, Evolutionary Algorithms are used to automatically build a RBF NN that solves a specified problem. The evolutionary al gorithms are implemented using a new evolutionary computation framework called EO, which allows direct evolution of problem solutions, so that no internal representation is needed, and specific solution domain knowledge can beused to construct evolutionary operators, as well as cost or fitness functions. Results show that this new approach finds nets with good generalization power, while maintaining a reasonable size.