Atlantis Institut des Sciences Fictives

Resumen: µG-ELM is a multiobjective evolutionary algorithm which looks for the best (in terms of the MSE) and most compact artificial neural network using the ELM methodology. In this work we present the µG2-ELM, an upgraded version of µG-ELM, previously presented by the authors. The upgrading is based on three key elements: a specifically designed approach for the initialization of the weights of the initial artificial neural networks, the introduction of a re-sowing process when selecting the population to be evolved and a change of the process used to modify the weights of the artificial neural networks. To test our proposal we consider several state-of-the-art Extreme Learning Machine (ELM) algorithms and we confront them using a wide and well-known set of continuous, regression and classification problems. From the conducted experiments it is proved that the µG2-ELM shows a better general performance than the previous version and also than other competitors. Therefore, we can guess that the combination of evolutionary algorithms with the ELM methodology is a promising subject of study since both together allow for the design of better training algorithms for artificial neural networks.
Idioma: Inglés
DOI: 10.1016/j.neucom.2015.07.069
Año: 2016
Publicado en: NEUROCOMPUTING 171 (2016), 1302-1312
ISSN: 0925-2312
Factor impacto JCR: 3.317 (2016)
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 24 / 133 = 0.18 (2016) - Q1 - T1
Factor impacto SCIMAGO: 0.879 - Artificial Intelligence (Q1) - Computer Science Applications (Q1) - Cognitive Neuroscience (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E22
Financiación: info:eu-repo/grantAgreement/ES/DGA/E58
Tipo y forma: Article (PostPrint)
Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Exportado de SIDERAL (2020-02-21-13:49:03)


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