The weighted majority algorithm
Information and Computation
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
Bias/variance analyses of mixtures-of-experts architectures
Neural Computation
Optimal linear combinations of neural networks
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
A Parallel Genetic Algorithm for Concept Learning
Proceedings of the 6th International Conference on Genetic Algorithms
Evolutionary induction of sparse neural trees
Evolutionary Computation
Boosting and other ensemble methods
Neural Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Committee machines are known to improve the performance of individual learners. Evolutionary algorithms generate multiple individuals that can be combined to build committee machines. However, it is not easy to decide how big the committee should be and what members constitute the best committee. In this paper, we present a probabilistic search method for determining the size and members of the committees of individuals that are evolved by a standard GP engine. Applied to a suite of benchmark learning tasks, the GP committees achieved significant improvement in prediction accuracy.