Machine learning techniques to select a reduced and optimal set of sensors for the design of Ad Hoc sensory systems, Quercia, L., and Palumbo Domenico , Lecture Notes in Electrical Engineering, Volume 539, p.405-416, (2019)

Electricity demand modelling with genetic programming, Castelli, M., De Felice Matteo, Manzoni L., Vanneschi L., Machado P., Costa E., and Cardoso A. , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 9273, p.213-225, (2015)

Forecasting short-term electricity consumption using a semantics-based genetic programming framework: The South Italy case, Castelli, M., Vanneschi L., and De Felice Matteo , Energy Economics, Volume 47, p.37-41, (2015)

Soft computing based optimization of combined cycled power plant start-up operation with fitness approximation methods, Bertini, I., De Felice Matteo, Pannicelli A., and Pizzuti S. , Applied Soft Computing Journal, Volume 11, Number 6, p.4110-4116, (2011)

Ambient temperature modelling with soft computing techniques, Bertini, I., Ceravolo F., Citterio M., De Felice Matteo, Di Pietra B., Margiotta F., Pizzuti S., and Puglisi G. , Solar Energy, Volume 84, Number 7, p.1264-1272, (2010)

Combining back-propagation and genetic algorithms to train neural networks for start-up time modeling in combined cycle power plants, Bertin, I., De Felice Matteo, and Pizzuti S. , Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010, p.165-170, (2010)