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Universidade do Minho > Escola de Engenharia da Universidade do Minho | School of Engineering at the University of Minho > Departamento de Sistemas de Informação > DSI - Engenharia da Programação e dos Sistemas Informáticos >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/8028

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Title: Evolution of neural networks for classification and regression
Authors: Rocha, Miguel
Cortez, Paulo, 1971-
Neves, José
Keywords: Supervised learning
Multilayer perceptrons
Evolutionary algorithms
Lamarckian optimization
Neural network ensembles
Issue date: 2007
Publisher: Elsevier
Citation: "Neurocomputing". ISSN 0925-2312. 70:16-18 (Aug. 2007) 2809-2816.
Abstract: Although Artificial Neural Networks (ANNs) are important Data Mining techniques, the search for the optimal ANN is a challenging task: the ANN should learn the input-output mapping without overfitting the data and training algorithms may get trapped in local minima. The use of Evolutionary Computation (EC) is a promising alternative for ANN optimization. This work presents two hybrid EC/ANN algorithms: the first evolves neural topologies while the latter performs simultaneous optimization of architectures and weights. Sixteen real-world tasks were used to test these strategies. Competitive results were achieved when compared with a heuristic model selection and other Data Mining algorithms.
Type: article
URI: http://hdl.handle.net/1822/8028
ISSN: 0925-2312
Publisher version: http://www.sciencedirect.com/science/journal/09252312
Peer-Reviewed: yes
Appears in Collections:DSI - Engenharia da Programação e dos Sistemas Informáticos
DI/CCTC - Artigos (papers)
CAlg - Artigos em revistas internacionais/Papers in international journals

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