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)


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).


artificial life complex networks neural networks 


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  1. 1.
    Yao, X.: Evolving Artificial Neural Networks. Proceedings of the IEEE 87(9), 1423–1447 (1999)CrossRefGoogle Scholar
  2. 2.
    Alander, J.T.: An indexed bibliography of genetic algorithms and neural networks, Technical Report 94-1-NN, University of Vaasa, Department of Information Technology and Production Economics (1998)Google Scholar
  3. 3.
    Cant-Paz, E., Kamath, C.: An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 915–927 (2005)Google Scholar
  4. 4.
    Watts, D.J., Strogatz, S.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  5. 5.
    Burns, G.A.P.C., Oung, M.P.Y: Analysis of the connectional organization of neural systems associated with the hippocampus in rats. Philosophical Transactions of the Royal Society B: Biological Sciences 355(1393), 55–70 (2000)CrossRefGoogle Scholar
  6. 6.
    Eguiluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M., Apkarian, A.V.: Scale-Free Brain Functional Networks. Phys. Rev. Lett. 94, 18–102 (2005)CrossRefGoogle Scholar
  7. 7.
    Dorogotvsev, S.N., Mendes, J.F.F.: Evolution of Networks. Advances in Physics 51(4), 1079–1187 (2002)CrossRefGoogle Scholar
  8. 8.
    Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Erdos, P., Renyi, A.: On random graphs, Publ. Math. Debrecen (1959)Google Scholar
  10. 10.
    Watts, D.J.: Small Worlds: The Dynamics of Networks Between Order and Randomness. Princeton University Press (1999)Google Scholar
  11. 11.
    Coven, R., Havlin, S., ben-Avraham, D.: Structural Properties of Scale-Free Networks. In: Bornholdt, S., Schuster, H.G. (eds.) Handbook of graphs and networks, ch. 4, Wiley-VCH, Chichester (2002)Google Scholar
  12. 12.
    Jeong, H., Neda, Z., Barabasi, A.-L.: Measuring preferential attachment for evolving networks. Euro. Phys. Lett. 61, 567 (2003)CrossRefGoogle Scholar
  13. 13.
    Langton, C.: Artificial Life. Addison-Wesley, Redwood City/CA, USA (1989)Google Scholar
  14. 14.
    Annunziato, M., Bertini, I., Lucchetti, M., Pannicelli, A., Pizzuti, S.: Adaptivity of Artificial Life Environment for On-Line Optimization of Evolving Dynamical Systems. In: Proc. EUNITE 2001, Tenerife, Spain (2001)Google Scholar
  15. 15.
    Annunziato, M., Bertini, I., Pannicelli, A., Pizzuti, S., Tsimring, L.: Complexity and Control of Combustion Processes in Industry. In: Proc. of CCSI 2000 Complexity and Complex System in Industry, Warwick, UK (2000)Google Scholar
  16. 16.
    Annunziato, M., Lucchetti, M., Orsini, G., Pizzuti, S.: Artificial life and on-line flows optimisation in energy networks. In: IEEE Swarm Intelligence Sympusium, Pasadena (CA), IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  17. 17.
    Annunziato, M., Bertini, I., Pannicelli, A., Pizzuti, S.: A Nature-inspired-Modeling-Optimization-Control system applied to a waste incinerator plant. In: 2nd European Symposium NiSIS’06, Puerto de la Cruz, Tenerife (Spain) (2006)Google Scholar
  18. 18.
    Annunziato, M., Bertini, I., Pannicelli, A., Pizzuti, S.: Evolutionary Control and On-Line Optimization of an MSWC Energy Process. Journal of Systemics, Cybernetics and Informatics 4(4) (2006)Google Scholar
  19. 19.
    Annunziato, M., Bertini, I., Iannone, R., Pizzuti, S.: Evolving feed-forward neural networks through evolutionary mutation parameters. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 554–561. Springer, Heidelberg (2006)Google Scholar
  20. 20.
    Annunziato, M., Bruni, C., Lucchetti, M., Pizzuti, S.: Artificial life approach for continuous optimisation of non stationary dynamical systems. Integrated Computer-Aided Engineering 10(2), 111–125 (2003)Google Scholar
  21. 21.
    Russo, L.P., Bequette, B.W.: Impact of process design on the multiplicity behaviour of a jacketed exothermic CSTR. AIChE Journal 41, 135–147 (1995)CrossRefGoogle Scholar
  22. 22.
    Saraf, V.S., Bequette, B.W.: Auto-tuning of cascade controlled open-loop unstable reactors. In: American Control Conference, Proceedings of the 2001, vol. 3, pp. 2021–2026 (2026)Google Scholar
  23. 23.
    Russo, L.P., Bequette, B.W.: State-Space versus Input/Output Representations for Cascade Control of Unstable Systems. Ind. Eng. Chem. Res. 36(6), 2271–2278 (1997)CrossRefGoogle Scholar

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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