Mauro Annunziato

Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling

In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach outperforms the best of the methods it puts together.

Urban Traffic Flow Forecasting Using Neural-Statistic Hybrid Modeling

In this paper we show a hybrid modeling approach which combines Artificial Neural Networks and a simple statistical approach in order to provide a one hour forecast of urban traffic flow rates. Experimentation has been carried out on three different classes of real streets and results show that the proposed approach clearly outperforms the best of the methods it combines.

The Evolutionary Control Methodology: An Overview

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 environmental changes). The essence could be synthesized in this way: not control rules but autonomous structures able to dynamically adapt and generate optimised-control rules. We tested the proposed methodology on two applications, the Chuas circuit and a combustion process in industrial incinerators which is being carried out. Experimentation concerned the on-line optimisation and adaptation of the process in different regimes without knowing the system equations and considering one parameter affected by unknown changes. Then we let the alife environment try to adapt to the new condition. Preliminary results show the system is able to dynamically adapt to slow environmental changes by recovering and tracking the optimal conditions.

Adaptive Parameterization of Evolutionary Algorithms and Chaotic Populations

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 according to some environmental factors. This paper deals with a new technique for setting the genetic parameters during the course of a run by adapting the population size and the operators rates on the basis of the environmental constrain of maximum population size. In addition genetic operators are seen as alternative reproduction strategies and fighting among individuals is introduced. Finally benchmarks of the proposed strategy on classical optimization problems are shown. The results show that the parameters reach an equilibrium point and that performances on the considered problems are very good.