KEYWORDS: Artificial Life, Adaptive Systems, On-line Optimisation, Evolutionary Computation, Continuos Learning, Dynamical Systems, Chua’s Circuit

The ideas proposed in this work are aimed to develop 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 (continuos learning directly from measurements) and capability to follow the system evolution (adaptation to environmental changes). The essence could be synthesised in this way: "not control rules but autonomous structures able to dynamically adapt and generate optimised-control rules". We tested our methodology on a simulator of a dynamical system: the Chua’s circuit. Experimentation concerned the on-line optimisation and adaptation of the system in different regimes without knowing the circuit’s equations. We considered one parameter affected by unknown changes and 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 the optimal condition.