A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
MIGA, A Software Tool for Nonlinear System Modelling with Modular Neural Networks
Applied Intelligence
Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms
Pattern Recognition Letters
Evolutionary product unit based neural networks for regression
Neural Networks
Information Sciences: an International Journal
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
Evolutionary product-unit neural networks classifiers
Neurocomputing
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
AG-ART: An adaptive approach to evolving ART architectures
Neurocomputing
Evolutionary Artificial Neural Network Design and Training for wood veneer classification
Engineering Applications of Artificial Intelligence
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Evolutionary induction of stochastic context free grammars
Pattern Recognition
Expert Systems with Applications: An International Journal
A two-stage algorithm in evolutionary product unit neural networks for classification
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
A genetic algorithm based approach to route selection and capacity flow assignment
Computer Communications
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Improvement of accuracy in a sound synthesis method using Evolutionary Product Unit Networks
Expert Systems with Applications: An International Journal
Evolutionary artificial neural networks: a review
Artificial Intelligence Review
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods