μG2-ELM: An upgraded implementation of μ G-ELM

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.