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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)
In this paper we discuss novel methods of classification and prediction of spatio-temporal dynamics in extended systems. We tested these methods on simulated data for the Kuramoto-Sivashinsky equation that describes unstable flame front propagation in uniform mixtures.
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)
During last decades there has been an increasing interest in artificially combining evolution and learning, in order to pursue adaptivity and to increase efficiency of control, supervision and optimisation systems. In particular the need for adaptation came out from several real-world applications in non-stationary environments ranging from non linear… (More)
This paper reports some applications of artificial life environments for the development of solutions based on the evolutionary properties. We show how this approach is able to create complex structures and how they can be used to solve optimization problems and applied to the process control and optimization.
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)