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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)
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)
A stochastic hybrid model for the production of the antibiotic subtilin by the Bacillus subtilis is investigated. This model consists of 5 variables with four possible discrete dynamical states and this high dimensionality represents a bottleneck for using statistical tools that require to solve the corresponding Fokker-Planck problem. For this reason, a(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)