Does Training Lead to the Formation of Modules in Threshold Networks?

  • D. Nicolay
  • A. Roli
  • T. CarlettiEmail author
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


This paper addresses the question to determine the necessary conditions for the emergence of modules in the framework of artificial evolution. In particular, threshold networks are trained as controllers for robots able to perform two different tasks at the same time. It is shown that modules do not emerge under a wide set of conditions in our experimental framework. This finding supports the hypothesis that the emergence of modularity indeed depends upon the algorithm used for artificial evolution and the characteristics of the tasks.


Hide Node Random Network Robot Performance Small Network Training Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research used computational resources of the “Plateforme Technologique de Calcul Intensif (PTCI)” located at the University of Namur, Belgium, which is supported by the F.R.S.-FNRS. This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimisation), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office.


  1. 1.
    Beaumont, M.A.: Evolution of optimal behaviour in networks of boolean automata. J. Theor. Biol. 165, 455–476 (1993)CrossRefGoogle Scholar
  2. 2.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  3. 3.
    Bullinaria, J.A.: Understanding the emergence of modularity in neural systems. Cogn. Sci. 31(4), 673–695 (2007)CrossRefGoogle Scholar
  4. 4.
    Clune, J., Mouret, J.-B., Lipson, H.: The evolutionary origins of modularity. Proc. R. Soc. B: Biol. Sci. 280(1755), 20122863 (2013)CrossRefGoogle Scholar
  5. 5.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley and Sons Ltd, Chichester (2008)Google Scholar
  6. 6.
    Geary, D.C., Huffman, K.J.: Brain and cognitive evolution: forms of modularity and functions of mind. Psychol. Bull, 128(5), 667 (2002)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)Google Scholar
  8. 8.
    Kashtan, N., Alon, U.: Spontaneous evolution of modularity and network motifs. Proc. Nat. Acad. Sci. USA 102(39), 13773–13778 (2005)ADSCrossRefGoogle Scholar
  9. 9.
    Nicolay, D., Carletti, T.: Neural networks learning: Some preliminary results on heuristic methods and applications. In: Perotti, A., Di Caro, L. (eds.) DWAI@AI*IA, volume 1126 of CEUR Workshop Proceedings, pp. 30–40. (2013)Google Scholar
  10. 10.
    Nicolay, D., Roli, A., Carletti, T.: Learning multiple conflicting tasks with artificial evolution. In Advances in Artificial Life and Evolutionary Computation, volume 445 of Communications in Computer and Information Science, pp. 127–139. Springer International Publishing (2014)Google Scholar
  11. 11.
    Peretto, P.: An Introduction to the Modeling of Neural Networks. Alea Saclay. Cambridge University Press, Cambridge (1992)CrossRefzbMATHGoogle Scholar
  12. 12.
    Rojas, R.: Neural Networks: A Systematic Introduction. Springer, Berlin (1996)CrossRefzbMATHGoogle Scholar
  13. 13.
    Seok, B.: Diversity and unity of modularity. Cogn. Sci. 30(2), 347–380 (2006)CrossRefGoogle Scholar
  14. 14.
    Villani, M., et al.: The detection of intermediate-level emergent structures and patterns. Adv. Artif. Life, ECAL 12, 372–378 (2013)Google Scholar
  15. 15.
    Villani, M. et al.: The search for candidate relevant subsets of variables in complex systems. Artificial Life, 2015. Accepted. Also available as arXiv:1502.01734
  16. 16.
    Wagner, G.P., Pavlicev, M., Cheverud, J.M.: The road to modularity. Nat. Rev. Genet. 8(12), 921–931 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mathematics and naXysUniversity of NamurNamurBelgium
  2. 2.Department of Computer Science and Engineering (DISI)University of Bologna, Campus of CesenaBolognaItaly

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