Mauro Annunziato

Learn More
The problem of identification of flow regimes in processes involving multiphase mixtures (nuclear plant, fluidization, hydrocarbons, chemical reactors) is an open problem for many industrial applications. Generally, different flow regimes induce different performances of the system. Due to the highly non-linear nature of the forces which rule the flow(More)
running a genetic algorithm entails setting a number of parameter values. Finding settings that work well on one problem is not a trivial task and a genetic algorithm performance can be severely impacted. Moreover we know that in natural environments population sizes, reproduction and competition rates, change and tend to stabilise around appropriate values(More)
In this paper we show different evolutionary algorithms in order to optimise on-line weights of feed-forward neural networks when applied to short term (20 min.) urban traffic prediction. We compare the evolutionary methods with the classical back-propagation algorithm and we show results when weights are off-line and on-line evolved. Preliminary results(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)
The extensive use of energy generation processes presents a severe challenge to the environment and makes indispensable to focus the research on the maximization of the energy efficiency and minimization of environmental impact like NOx and CO emissions. Therefore, in this paper we report our experience of a Nature-inspired-Modelling-Optimisation-Control(More)