A hybrid intelligent system that combines neural networks and genetic algorithms for feature selection is proposed in this paper. In the world of companies, it is widely acknowledged the importance of knowledge management KM in terms of organisation survival and maintenance of competitive strength. Furthermore, in the energy sector, it is even more critical as energy is a strategic national industry for most countries. However, there are not enough tools with which to systematise the work of KM and to provide support for decision-making. The present study proposes a tool that addresses the problem of selecting the features of KM from the immense amount of available data that would provide managers with most valuable information. The developed system has been applied to real-world data from a power plant, to mine deep knowledge on that KM data, and to select the most informative features. Experimental results justify a meaningful subset of features in the opinion of KM experts, and validate the proposed hybrid system.
['Lourdes Sáiz-Bárcena', 'Álvaro Herrero', 'Miguel A. Campo', 'Ricardo del Olmo Martínez']
Easing knowledge management in the power sector by means of a neuro-genetic system