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

TitleEvolution of neural networks for classification and regression
Author(s)Rocha, Miguel
Cortez, Paulo
Neves, José
KeywordsSupervised learning
Multilayer perceptrons
Evolutionary algorithms
Lamarckian optimization
Neural network ensembles
Issue date2007
PublisherElsevier
JournalNeurocomputing
Citation"Neurocomputing". ISSN 0925-2312. 70:16-18 (Aug. 2007) 2809-2816.
Abstract(s)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.
Typearticle
URIhttp://hdl.handle.net/1822/8028
DOI10.1016/j.neucom.2006.05.023
ISSN0925-2312
Publisher versionhttp://www.sciencedirect.com/science/journal/09252312
Peer-Reviewedyes
AccessopenAccess
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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