Neural networks and the bias/variance dilemma
Neural Computation
Modeling with constructive backpropagation
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A Multi-Objective Optimization Approach for Training Artificial Neural Networks
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Multi-objective genetic algorithms to create ensemble of classifiers
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
A memetic model of evolutionary PSO for computational finance applications
Expert Systems with Applications: An International Journal
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This paper will investigate the application of multiobjective evolutionary neural networks in time series forecasting. The proposed algorithmic model considers training and validation accuracy as the objectives to be optimized simultaneously, so as to balance the accuracy and generalization of the evolved neural networks. To improve the overall generalization ability for the set of solutions attained by the multiobjective evolutionary optimizer, a simple algorithm to filter possible outliers, which tend to deteriorate the overall performance, is proposed also. Performance comparison with other existing evolutionary neural networks in several time series problems demonstrates the practicality and viability of the proposed time series forecasting model.