The use of coevolution and the artificial immune system for ensemble learning |
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Authors: | Bruno H G Barbosa Lam T Bui Hussein A Abbass Luis A Aguirre Ant?nio P Braga |
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Affiliation: | (1) Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil;(2) School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, ACT, Australia;(3) Department of Engineering, Federal University of Lavras, Lavras, MG, Brazil |
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Abstract: | This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial
immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm.
An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles
is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is
responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the
differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is
evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller
ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of
the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles
may be found using only implicit diversity promotion techniques. |
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