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Evolving Complex Neural Networks

  • Mauro Annunziato
  • Ilaria Bertini
  • Matteo De Felice
  • Stefano Pizzuti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4733)

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).

Keywords

artificial life complex networks neural networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mauro Annunziato
    • 1
  • Ilaria Bertini
    • 1
  • Matteo De Felice
    • 2
  • Stefano Pizzuti
    • 1
  1. 1.Energy, New technology and Environment Agency (ENEA), Via Anguillarese 301, 00123 RomeItaly
  2. 2.Dipartimento di Informatica ed Automazione, Università degli Studi di Roma “Roma Tre”, Via della Vasca Navale 79, 00146 RomeItaly

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