Mauro Annunziato

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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)
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)
Complex networks like the scale-free model proposed by BarabasiAlbert 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)
In most of industrial applications and in the ®elds of scienti®c research phenomena are highly non-linear and/or they have high dimensionality. In such cases a model which describes exactly the phenomenon is very hard to de®ne, but often many simpli®ed models describing the problem's phenomenology in particular conditions are available. The problem of the(More)
ParticipART is an initiative aimed at exploring participation in interactive works using ubiquitous computing and mixed reality. It supports and analyses work of artists and creative practitioners incorporating or reflecting on participatory processes to support new roles and forms of engagement for art participants. We aim at proposing a space for(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)