Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Ensemble learning via negative correlation
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Ensembling neural networks: many could be better than all
Artificial Intelligence
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation
Applied Soft Computing
EROS: Ensemble rough subspaces
Pattern Recognition
Face Detection Using Mixture of MLP Experts
Neural Processing Letters
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The formation of ensemble of artificial neural networks has attracted attentions of researchers in the machine learning and statistical inference domains. It has been shown that combining different neural networks could improve the generalization ability of the learning machine. One challenge is when to stop the training or evolution of the neural networks to avoid overfitting. In this paper, we show that different early stopping criteria based on (i) the minimum validation fitness of the ensemble, and (ii) the minimum of the average population validation fitness could generalize better than the survival population in the last generation. The proposition was tested on four different ensemble methods: (i) a simple ensemble method, where each individual of the population (created and maintained by the evolutionary process) is used as a committee member, (ii) ensemble with island model as a diversity promotion mechanism, (iii) a recent successful ensemble method namely ensemble with negative correlation learning and (iv) an ensemble formed by applying multi-objective optimization. The experimental results suggested that using minimum validation fitness of the ensemble as an early stopping criterion is beneficial.