NEAT in HyperNEAT Substituted with Genetic Programming

  • Zdeněk Buk
  • Jan Koutník
  • Miroslav Šnorek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5495)


In this paper we present application of genetic programming (GP) [1] to evolution of indirect encoding of neural network weights. We compare usage of original HyperNEAT algorithm with our implementation, in which we replaced the underlying NEAT with genetic programming. The algorithm was named HyperGP. The evolved neural networks were used as controllers of autonomous mobile agents (robots) in simulation. The agents were trained to drive with maximum average speed. This forces them to learn how to drive on roads and avoid collisions. The genetic programming lacking the NEAT complexification property shows better exploration ability and tends to generate more complex solutions in fewer generations. On the other hand, the basic genetic programming generates quite complex functions for weights generation. Both approaches generate neural controllers with similar abilities.


Neural Network Genetic Programming Mobile Agent Recurrent Neural Network Neural Controller 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zdeněk Buk
    • 1
  • Jan Koutník
    • 1
  • Miroslav Šnorek
    • 1
  1. 1.Computational Intelligence Group Department of Computer Science and Engineering Faculty of Electrical EngineeringCzech Technical University in PragueCzechia

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