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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
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.
Publisher version
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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