Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/353
Título: | A Lamarckian Approach for Neural Network Training |
Autor(es): | Cortez, Paulo Rocha, Miguel Neves, José |
Palavras-chave: | feedforward neural networks genetic and evolutionary algorithms hybrid systems lamarckian optimization learning algorithms |
Data: | 2002 |
Editora: | Springer |
Revista: | Neural Processing Letters |
Citação: | "Neural Processing Letters". 15:2 (2002) 105-116. |
Resumo(s): | In Nature, living beings improve their adaptation to surrounding environments by means of two main orthogonal processes: evolution and lifetime learning. Within the Artificial Intelligence arena, both mechanisms inspired the development of non-orthodox problem solving tools, namely: Genetic and Evolutionary Algorithms (GEAs) and Artificial Neural Networks (ANNs). In the past, several gradient-based methods have been developed for ANN training, with considerable success. However, in some situations, these may lead to local minima in the error surface. Under this scenario, the combination of evolution and learning techniques may induce better results, desirably reaching global optima. Comparative tests that were carried out with classification and regression tasks, attest this claim. |
Tipo: | Artigo |
Descrição: | Prova tipográfica (In Press). |
URI: | https://hdl.handle.net/1822/353 |
DOI: | 10.1023/A:1015259001150 |
ISSN: | 1370-4621 |
Versão da editora: | The original publication is available at www.springerlink.com |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals DI/CCTC - Artigos (papers) DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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lamnpl2.pdf Acesso restrito! | 270,91 kB | Adobe PDF | Ver/Abrir |