65228 20200221144339.0 doi 10.1016/j.neucom.2015.07.069 sideral 92294 ART-2016-92294 eng (orcid)0000-0001-8749-8291 Lacruz, B. Universidad de Zaragoza µG2-ELM: an upgraded implementation of µ G-ELM 2016 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/ES/DGA/E22 info:eu-repo/grantAgreement/ES/DGA/E58 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 3.317 2016 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 24 / 133 = 0.18 2016 Q1 T1 0.879 2016 Artificial Intelligence 2016 Q1 Computer Science Applications 2016 Q1 Cognitive Neuroscience 2016 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion (orcid)0000-0003-1320-0223 Lahoz, D. Universidad de Zaragoza (orcid)0000-0003-2988-7728 Mateo, P. M. Universidad de Zaragoza 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 171 (2016), 1302-1312 Neurocomputing NEUROCOMPUTING 0925-2312 742040 http://zaguan.unizar.es/record/65228/files/texto_completo.pdf Postprint 124260 http://zaguan.unizar.es/record/65228/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:65228 articulos driver 2020-02-21-13:49:03 ARTICLE