Evolved neural networks based on cellular automata for sensory-motor controller

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@Article{Kim:2006:Neurocomputing,
  author =       "Kyung-Joong Kim and Sung-Bae Cho",
  title =        "Evolved neural networks based on cellular automata for
                 sensory-motor controller",
  journal =      "Neurocomputing",
  year =         "2006",
  volume =       "69",
  number =       "16-18",
  pages =        "2193--2207",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 neural network, Incremental evolution, Multi-module
                 integration, Cellular automata, Mobile robot control",
  DOI =          "doi:10.1016/j.neucom.2005.07.013",
  abstract =     "Constructing the controller of a mobile robot has
                 several issues to be addressed: how to automate
                 behaviour generation procedure, how to insert available
                 domain knowledge effectively, and how to hybrid these
                 methods in an integrated manner. There has been
                 extensive work to construct an optimal neural network
                 for controlling a mobile robot by evolutionary
                 approaches such as genetic algorithm, genetic
                 programming, and so on. However, evolutionary
                 approaches have a difficulty to design the controller
                 that conducts complex behaviours. In order to overcome
                 this shortcoming, we propose an incremental evolution
                 method for neural networks based on cellular automata
                 and a method of combining several evolved modules by a
                 rule-based approach. The incremental evolution method
                 evolves the neural network by starting with simple
                 environment and gradually making it more complex. The
                 multi-modules integration method can make complex
                 behaviors by combining several modules evolved or
                 programmed to do simple behaviours. Simulation results
                 show the potential of the incremental evolution and
                 multi-module integration methods as sophisticated
                 techniques to make the evolved neural network to do
                 complex behaviours. In this paper, we attempt to
                 investigate the applicability of cellular
                 automata-based neural networks and propose
                 sophisticated techniques for the generation of
                 high-level behaviours.",
}

Genetic Programming entries for Kyung-Joong Kim Sung Bae Cho

Citations