Evolving Complex Neural Networks

Abstract. Complex networks like the scale-free model proposed by BarabasiAlbert 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 pe...

• Type: Conference
• Year: 2007
• Where: AIIA
• Authors: Mauro Annunziato, Ilaria Bertini, Matteo De Felice, Stefano Pizzuti
• Keywords: AIIA 2007, Artificial Intelligence, Complex Networks, Neural Networks, Topology