Information Sciences: an International Journal
Configuring an evolutionary tool for the inventory and transportation problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
A general framework for statistical performance comparison of evolutionary computation algorithms
Information Sciences: an International Journal
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A robust knowledge-based plant searching strategy
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of HLVQ and G-Prop Neural Networks to the Problem of Bankruptcy Prediction
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
A statistical model of pollution-caused pulmonary crises
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
A study on the use of statistical tests for experimentation with neural networks
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Architecture performance prediction using evolutionary artificial neural networks
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Time-dependent performance comparison of evolutionary algorithms
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
Statistical analysis of parameter setting in real-coded evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Classification by evolutionary generalised radial basis functions
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Online vs. offline ANOVA use on evolutionary algorithms
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Computers in Biology and Medicine
Evolutionary design of a brain-computer interface
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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Interest in hybrid methods that combine artificial neural networks and evolutionary algorithms has grown in the last few years, due to their robustness and ability to design networks by setting initial weight values, by searching the architecture and the learning rule and parameters. This paper presents an exhaustive analysis of the G-Prop method, and the different parameters the method requires (population size, selection rate, initial weight range, number of training epochs, etc.) are determined. The paper also the discusses the influence of the application of genetic operators on the precision (classification ability or error) and network size in classification problems. The significance and relative importance of the parameters with respect to the results obtained, as well as suitable values for each, were obtained using the ANOVA (analysis of the variance). Experiments show the significance of parameters concerning the neural network and learning in the hybrid methods. The parameters found using this method were used to compare the G-Prop method both to itself with other parameter settings, and to other published methods.