Evolving Modularity in Robot Behaviour Using Gene Expression Programming

  • Jonathan Mwaura
  • Ed Keedwell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6856)


Incremental learning [3] and layered learning [4] have been proposed as suitable approaches to improve evolutionary robotic (ER) algorithms by subdividing the required behaviour into simpler tasks. However, incremental learning does not divide the controller to unique task modules and although layered learning subdivides the problem into modules it does not offer continuous learning for the various sub-behaviours. Moreover, both methods involve the modification of the fitness function in every module thus increasing computational overhead.


Genetic Programming Obstacle Avoidance Gene Expression Programming Computational Overhead Incremental Learning 
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    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. The MIT Press, Cambridge (2000)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jonathan Mwaura
    • 1
  • Ed Keedwell
    • 1
  1. 1.College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK

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