Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/2219| Título: | Evolutionary neural network learning |
| Autor(es): | Rocha, Miguel Cortez, Paulo Neves, José |
| Palavras-chave: | Neural network training MultiLayer perceptrons Evolutionary algorithms Lamarckian optimization |
| Data: | 4-Dez-2003 |
| Editora: | Springer |
| Revista: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Citação: | PORTUGUESE CONFERENCE ON ARTIFICIAL INTELLIGENCE (EPIA), 11, Beja, 2003 - "Progress in artificial intelligence : proceedings". Heidelberg : Springer, 2003. ISBN 3-540-20589-6. p. 24.28. |
| Resumo(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. |
| Tipo: | Capítulo de livro |
| URI: | https://hdl.handle.net/1822/2219 |
| ISBN: | 3-540-20589-6 |
| ISSN: | 0302-9743 |
| Versão da editora: | The original publication is available at www.springerlink.com |
| Arbitragem científica: | yes |
| Acesso: | Acesso aberto |
| Aparece nas coleções: | DI/CCTC - Artigos (papers) DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| alea03.pdf | 157,81 kB | Adobe PDF | Ver/Abrir |
