Abstract. Complex networks like the scale-free model proposed by Barabasi-Albert 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 of modeling a chemical process with the presence of unknown inputs (disturbance). The evolutionary algorithm we use considers an initial population of individuals with differents scale-free networks in the genotype and at the end of the algorithm we observe and analyze the topology of networks with the best performances. Experimentation on modeling a complex chemical process shows that performances of networks with complex topology are similar to the feed-forward ones but the analysis of the topology of the most performing networks leads to the conclusion that the distribution of input node information affects the network performance (modeling capability).

mutation parameters

Abstract. This paper describes the use of evolutionary methods inspired by artificial life environments for the development of solutions connected to combustion problems based on the evolutionary properties. The idea proposed in this paper is aimed to developing a new approach to the optimisation and control of complex processes for energy production/consumption. This methodology is based on evolutionary optimisation and it started from some successful experiences in the dynamic characterisation (for diagnostics and control) for at least two industrial applications (oil field diagnostics and combustion dynamic characterisation). Furthermore an optimisation study has shown very interesting features of artificial life environments. The basic features of the proposed approach are: • dynamics based • no intensive modelling (progressive training directly from the measurements) • able to follow the plant evolution The essence of this approach could be synthesised by the following sentence: "not control rules but autonomous structures able to generate optimized-control rules". Good results have been obtained for dynamic analysis using the dynamic moments technique based on detection of attractor morphology. The results obtained for flame dynamics characterization are resulted better than the more classical nonlinear discriminants. The driving process is the dynamic building of a model on the basis of the observation of the effects that the regulation actions (acted by the operators or any other existing control systems) have on the plant performance. The powerful of optimization have been shown by artificial life: in the standard Traveling Salesman Problem the obtained results are surely comparable (better in some cases in respect to other algorithms). The proposed approach has been applying to a real scale waste incinerator in the