Arithmetic Dynamical Genetic Programming in the XCSF Learning Classifier System

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@InProceedings{Preen:2011:ADGPitXLCS,
  title =        "Arithmetic Dynamical Genetic Programming in the XCSF
                 Learning Classifier System",
  author =       "Richard J. Preen and Larry Bull",
  pages =        "1427--1434",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
                 Computation",
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, XCSF learning
                 classifier system, arithmetic dynamical genetic
                 programming, condition-action production system rules,
                 continuous-valued dynamical system representation,
                 nonlinear continuous-valued reinforcement learning
                 problem, open-ended evolution, polynomial regression
                 tasks, learning systems, pattern classification,
                 polynomial approximation, regression analysis",
  DOI =          "doi:10.1109/CEC.2011.5949783",
  abstract =     "This paper presents results from an investigation into
                 using a continuous-valued dynamical system
                 representation within the XCSF Learning Classifier
                 System. In particular, dynamical arithmetic genetic
                 networks are used to represent the traditional
                 condition-action production system rules. It is shown
                 possible to use self-adaptive, open-ended evolution to
                 design an ensemble of such dynamical systems within
                 XCSF. The results presented herein show that the
                 collective emergent behaviour of the evolved systems
                 exhibits competitive performance with those previously
                 reported on a non-linear continuous-valued
                 reinforcement learning problem. In addition, the
                 introduced system is shown to provide superior
                 approximations to a number of composite polynomial
                 regression tasks when compared with conventional
                 tree-based genetic programming.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",
}

Genetic Programming entries for Richard Preen Larry Bull

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