Artificial life approach for continuous optimisation of non-stationary dynamical systems

Authors:
M. Annunziato;C. Bruni;M. Lucchetti;S. Pizzuti
Affiliations:
ENEA - Energy, New technologies, Environment Agency, 'Casaccia' R.C., Via Anguillarese 301, 00060 Rome, Italy. Tel.: +39 06 3048 4411/ Fax: +39 06 3048 4811/ E-mail: {mauro.annunziato, stefano.piz ...;Department of Computer and Systems Science, University of Rome 'La Sapienza', Via Eudossiana 18, 00184 Rome, Italy. Tel.: +39 06 4458 5938/ Fax: +39 06 4458 5367/ E-mail: {brunic, lucchetti}@dis.u ...;(Correspd.) Department of Computer and Systems Science, University of Rome 'La Sapienza', Via Eudossiana 18, 00184 Rome, Italy. Tel.: +39 06 4458 5938/ Fax: +39 06 4458 5367/ E-mail: {brunic, lucc ...;ENEA - Energy, New technologies, Environment Agency, `Casaccia' R.C., Via Anguillarese 301, 00060 Rome, Italy. Tel.: +39 06 3048 4411/ Fax: +39 06 3048 4811/ E-mail: {mauro.annunziato, stefano.piz ...
Venue:
Integrated Computer-Aided Engineering
Year:
2003

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Abstract

In this paper, we develop an intelligent system to approach dynamical optimisation problems emerging in control of complex systems. In particular our proposal is to exploit the adaptivity of an artificial life (alife) environment in order to achieve "not control rules but autonomous structures able to dynamically adapt and to generate optimised-control rules". 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 suggested methodology has been tested on an energy regulation problem deriving from a classical testbed in dynamical systems experimentations: the Chua's circuit. We supposed not to know the system dynamics and to be able to act only on a subset of control parameters, letting the others vary in time in a random discrete way. We let the optimisation process searching for the new best value of performance, whenever a drop due to changes in fitness landscape occurred. We present the most important results showing the effectiveness of the proposed approach in adapting to environmental non-stationary changes by recovering the optimal value of process performance.