doi:10.1016/j.neucom.2015.07.069engLacruz, B.Lahoz, D.Mateo, P. M.µG2-ELM: an upgraded implementation of µ G-ELMART-2016-92294µG-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.2016http://zaguan.unizar.es/record/6522810.1016/j.neucom.2015.07.069http://zaguan.unizar.es/record/65228oai:zaguan.unizar.es:65228info:eu-repo/grantAgreement/ES/DGA/E22info:eu-repo/grantAgreement/ES/DGA/E58NEUROCOMPUTING 171 (2016), 1302-1312by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess