A tolerant algorithm for linearly constrained optimization calculations
Mathematical Programming: Series A and B
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evolving the Topology and the Weights of Neural Networks Using a Dual Representation
Applied Intelligence
Time Series Forecasting Using Massively Parallel Genetic Programming
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
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
IEEE Transactions on Evolutionary Computation
Evolutionary Design of Neural Network Architectures Using a Descriptive Encoding Language
IEEE Transactions on Evolutionary Computation
Evolutionary neural networks for anomaly detection based on the behavior of a program
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Artificial neural networks for solving ordinary and partial differential equations
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Location of amide I mode of vibration in computed data utilizing constructed neural networks
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Vector-valued function estimation by grammatical evolution for autonomous robot control
Information Sciences: an International Journal
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The term neural network evolution usually refers to network topology evolution leaving the network's parameters to be trained using conventional algorithms. In this paper we present a new method for neural network evolution that evolves the network topology along with the network parameters. The proposed method uses grammatical evolution to encode both the network and the parameters space. This allows for a better description of the network using a formal grammar allowing the network architect to shape the resulting search space in order to meet each problem requirement. The proposed method is compared with other three methods for neural network training and is evaluated using 9 known classification problems and 9 known regression problems. In all 18 datasets, the proposed method outperforms its competitors.