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

• Published in: Neurocomputing (Amsterdam) Vol. 171; pp. 1302 - 1312
• Main Authors: Lacruz, B., Lahoz, D., Mateo, P.M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2016
• Subjects: Artificial neural networks; Decision theory; Extreme Learning Machines; Multi-objective evolutionary algorithms; Extreme Learning Machines; Multi-objective evolutionary algorithms; Decision theory; Artificial neural networks
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2015.07.069

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.