On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems

  • Larry Bull
  • Richard Preen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)


Many representations have been presented to enable the effective evolution of computer programs. Turing was perhaps the first to present a general scheme by which to achieve this end. Significantly, Turing proposed a form of discrete dynamical system and yet dynamical representations remain almost unexplored within genetic programming. This paper presents results from an initial investigation into using a simple dynamical genetic programming representation within a Learning Classifier System. It is shown possible to evolve ensembles of dynamical Boolean function networks to solve versions of the well-known multiplexer problem. Both synchronous and asynchronous systems are considered.


Genetic Programming Boolean Function Cellular Automaton Discrete Dynamical System Evolutionary Search 
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

  • Larry Bull
    • 1
  • Richard Preen
    • 1
  1. 1.Department of Computer ScienceUniversity of the West of EnglandBristolUK

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