Evolving Modularity in Robot Behaviour Using Gene Expression Programming

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

Abstract

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.

Keywords

Genetic Programming Obstacle Avoidance Gene Expression Programming Computational Overhead Incremental Learning 
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.

References

  1. 1.
    Ferreira, C.: Gene Expression programming: A new Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)MathSciNetMATHGoogle Scholar
  2. 2.
    Lazarus, C., Hu, H.: Using Genetic Programming to Evolve Robot Behaviours. In: Proceedings of the 3rd British Conference on Autonomous Mobile Robotics and Autonomous Systems, Manchester, UK (2001)Google Scholar
  3. 3.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. The MIT Press, Cambridge (2000)Google Scholar
  4. 4.
    Togelius, J.: Evolution of Subsumption Architecture Neurocontroller. J. Intelligent Fuzzy System 15, 15–20 (2004)Google Scholar

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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