Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/426

TitleEvolutionary neural network learning algorithms for changing environments
Author(s)Rocha, Miguel
Cortez, Paulo
Neves, José
KeywordsBaldwinian and Lamarckian effects
Evolutionary programming
Multilayer perceptrons
Issue dateApr-2004
PublisherWorld Scientific and Engineering Academy and Society (WSEAS)
Citation“WSEAS Transactions on Systems”. ISSN 1109-2777. 3:2 (2004) 596-601.
Abstract(s)Classical Machine Learning methods are usually developed to work in static data sets. Yet, real world data typically changes over time and there is the need to develop novel adaptive learning algorithms. In this work, a number of algorithms, combining Neural Network learning models and Evolutionary Computation optimization techniques, are compared, being held several simulations based on artificial and real world problems. The results favor the combination of evolution and lifetime learning according to the Baldwin effect framework.
TypeArticle
URIhttp://hdl.handle.net/1822/426
ISSN1109-2777
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
CAlg - Artigos em livros de atas/Papers in proceedings
DI/CCTC - Artigos (papers)
DSI - Engenharia da Programação e dos Sistemas Informáticos

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