|Title||Evolving complex neural networks|
|Publication Type||Articolo su Rivista peer-reviewed|
|Year of Publication||2007|
|Authors||Annunziato, M., Bertini I., De Felice Matteo, and Pizzuti S.|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keywords||Artificial life, Biological systems, Complex networks, Evolutionary algorithms, Large scale systems, Neural networks, Topology|
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). © Springer-Verlag Berlin Heidelberg 2007.
cited By 3