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Most methods for the evolutionary generation of multi-layer perceptron classifiers use a divide-and-conquer strategy, where the tasks of feature selection, structure design, and weight training are performed separately. The concurrent evolution of the whole classifier has been seldom attempted and its effectiveness has never been exhaustively benchmarked.(More)
Parametric reduced-order model approach for simulation and optimization of aeroelastic systems with structural nonlinearities. General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: Explore Bristol Research is a digital archive(More)
This paper presents a novel version of the bees algorithm. This version is characterized by an extended set of search operators, and a mechanism that protects the most recently generated solutions from competition with more evolved individuals. Compared to the standard implementation of the bees algorithm, the new procedure requires the selection of an(More)
This paper presents FeaSANNT, an evolutionary procedure for feature selection and weight training for neural network classifiers. FeaSANNT exploits the global nature of evolutionary search to avoid sub-optimal peaks of performance. FeaSANNT was used to train a multi-layer perceptron classifier on seven benchmark problems. FeaSANNT attained accurate and(More)
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