Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/2222

TitleSimultaneous evolution of neural network topologies and weights for classification and regression
Author(s)Rocha, Miguel
Cortez, Paulo
Neves, José
KeywordsSupervised learning
Multilayer perceptrons
Evolutionary algorithms
Ensembles
Issue date8-Jun-2005
PublisherSpringer
JournalLecture Notes in Computer Science
CitationINTERNATIONAL WORK-CONFERENCE ON ARTIFICIAL NEURAL NETWORKS (IWANN), 8, Barcelona, 2005 - "Computational intelligence and bioinspired systems : proceedings". Heidelberg : Springer, 2005. ISBN 3-540-26208-3. p. 59-66.
Abstract(s)Artificial Neural Networks (ANNs) are important Data Mining (DM) techniques. Yet, the search for the optimal ANN is a challenging task: the architecture should learn the input-output mapping without overfitting the data and training algorithms tend to get trapped into local minima. Under this scenario, the use of Evolutionary Computation (EC) is a promising alternative for ANN design and training. Moreover, since EC methods keep a pool of solutions, an ensemble can be build by combining the best ANNs. This work presents a novel algorithm for the optimization of ANNs, using a direct representation, a structural mutation operator and Lamarckian evolution. Sixteen real-world classification/regression tasks were used to test this strategy with single and ensemble based versions. Competitive results were achieved when compared with a heuristic model selection and other DM algorithms.
TypeconferencePaper
URIhttp://hdl.handle.net/1822/2222
ISBN3-540-26208-3
ISSN0302-9743
Publisher versionThe original publication is available at http://www.springerlink.com
Peer-Reviewedyes
AccessopenAccess
Appears in Collections:DSI - Engenharia da Programação e dos Sistemas Informáticos
DI/CCTC - Artigos (papers)
CAlg - Artigos em livros de atas/Papers in proceedings

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