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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)

Abstract

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.

Keywords

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.

Notes

Acknowledgments

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.

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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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