Evolving complex neural networks

Mauro Annunziato, Ilaria Bertini, Matteo De Felice, Stefano Pizzuti

Research output: Contribution to conferencePaper

5 Citations (Scopus)

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). © Springer-Verlag Berlin Heidelberg 2007.
Original languageEnglish
Publication statusPublished - 2007
Externally publishedYes
Event10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007 - , Italy
Duration: 1 Jan 2007 → …

Conference

Conference10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007
CountryItaly
Period1/1/07 → …

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Annunziato, M., Bertini, I., De Felice, M., & Pizzuti, S. (2007). Evolving complex neural networks. Paper presented at 10th Congress of the Italian Association for Artificial Intelligence, AI IA 2007, Italy.