Mauro Annunziato

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This paper 7 presents the preliminary results of a joint research project about Smart Cities. This project is adopting a multidisciplinary approach that combines artificial intelligence techniques with psychology research to monitor the current state of the city of L'Aquila after the dreadful earthquake of April 2009. This work focuses on the description of(More)
In this paper we show a preliminary work on evolutionary mutation parameters in order to understand whether it is possible or not to skip mutation parameters tuning. In particular, rather than considering mutation parameters as global environmental features, we regard them as endogenous features of the individuals by putting them directly in the genotype.(More)
In this paper we propose a methodology to optimally manage and online control energy flows over a power network. Such a methodology is essentially based on a artificial life environment. Exploiting some results achieved in the field of evolutionary computing and artificial life environments, the proposed method is intended to combine the ability to select(More)
The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of thermal comfort for office building heated by gas. Although the accuracy of the forecasting is similar for both the feed-forward and the recurrent network, the removal of features leads to accuracy reduction much(More)
Complex networks like the scale-free model proposed by Barabasi-Albert are observed in many biological systems and the application of this topology to artificial neural network leads to interesting considerations. In this paper, we present a preliminary study on how to evolve neural networks with complex topologies. This approach is utilized in the problem(More)
The ideas proposed in this work are aimed to describe a novel approach based on artificial life (alife) environments for on-line adaptive optimisation of dynamical systems. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to(More)