Modular Design of Irreducible Systems

  • Martin Hülse
  • Frank Pasemann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


Strategies of incremental evolution of artificial neural systems have been suggested over the last decade to overcome the scalability problem of evolutionary robotics. In this article two methods are introduced that support the evolution of neural couplings and extensions of recurrent neural networks of general type. These two methods are applied to combine and extend already evolved behavioral functionality of an autonomous robot in order to compare the structure-function relations of the resulting networks with those of the initial structures. The results of these investigations indicate that the emergent dynamics of the resulting networks turn these control structures into irreducible systems. We will argue that this leads to several consequences. One is, that the scalability problem of evolutionary robotics remains unsolved, no matter which type of incremental evolution is applied.


Hide Neuron Basic Module Recurrent Neural Network Behavior Control Input Neuron 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Hülse
    • 1
  • Frank Pasemann
    • 1
  1. 1.Fraunhofer Institute for Autonomous Intelligent SystemsSankt AugustinGermany

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