Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/2219

TitleEvolutionary neural network learning
Author(s)Rocha, Miguel
Cortez, Paulo
Neves, José
KeywordsNeural network training
MultiLayer perceptrons
Evolutionary algorithms
Lamarckian optimization
Issue date4-Dec-2003
PublisherSpringer
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
CitationPORTUGUESE CONFERENCE ON ARTIFICIAL INTELLIGENCE (EPIA), 11, Beja, 2003 - "Progress in artificial intelligence : proceedings". Heidelberg : Springer, 2003. ISBN 3-540-20589-6. p. 24.28.
Abstract(s)Several gradient-based methods have been developed for Artificial Neural Network (ANN) training. Still, in some situations, such procedures may lead to local minima, making Evolutionary Algorithms (EAs) a promising alternative. In this work, EAs using direct representations are applied to several classification and regression ANN learning tasks. Furthermore, EAs are also combined with local optimization, under the Lamarckian framework. Both strategies are compared with conventional training methods. The results reveal an enhanced performance by a macro-mutation based Lamarckian approach.
TypeBook part
URIhttp://hdl.handle.net/1822/2219
ISBN3-540-20589-6
ISSN0302-9743
Publisher versionThe original publication is available at www.springerlink.com
Peer-Reviewedyes
AccessOpen access
Appears in Collections:DI/CCTC - Artigos (papers)
DSI - Engenharia da Programação e dos Sistemas Informáticos

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