Dynamics of Neuronal Models in Online Neuroevolution of Robotic Controllers

  • Fernando Silva
  • Luís Correia
  • Anders Lyhne Christensen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8154)


In this paper, we investigate the dynamics of different neuronal models on online neuroevolution of robotic controllers in multirobot systems. We compare the performance and robustness of neural network-based controllers using summing neurons, multiplicative neurons, and a combination of the two. We perform a series of simulation-based experiments in which a group of e-puck-like robots must perform an integrated navigation and obstacle avoidance task in environments of different complexity. We show that: (i) multiplicative controllers and hybrid controllers maintain stable performance levels across tasks of different complexity, (ii) summing controllers evolve diverse behaviours that vary qualitatively during task execution, and (iii) multiplicative controllers lead to less diverse and more static behaviours that are maintained despite environmental changes. Complementary, hybrid controllers exhibit both behavioural characteristics, and display superior generalisation capabilities in simple and complex tasks.


Evolutionary robotics artificial neural network evolutionary algorithm online neuroevolution 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fernando Silva
    • 1
    • 3
  • Luís Correia
    • 3
  • Anders Lyhne Christensen
    • 1
    • 2
  1. 1.Instituto de TelecomunicaçõesLisboaPortugal
  2. 2.Instituto Universitário de Lisboa (ISCTE-IUL)LisboaPortugal
  3. 3.LabMAg, Faculdade de CiênciasUniversidade de LisboaPortugal

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