Abstract

Abstract. 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 optimisedcontrol rules". We tested the proposed methodology on two applications, the Chua's 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.