Dynamical genetic programming in learning classifier systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Preen:thesis,
  author =       "Richard John Preen",
  title =        "Dynamical genetic programming in learning classifier
                 systems",
  school =       "University of the West of England",
  year =         "2011",
  address =      "Bristol, UK",
  keywords =     "genetic algorithms, genetic programming, artificial
                 genetic regulatory networks, knowledge representation,
                 learning classifer systems, xcs, xcsf",
  URL =          "http://eprints.uwe.ac.uk/25852/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=31&uin=uk.bl.ethos.557835",
  abstract =     "Learning Classifier Systems (LCS) traditionally use a
                 ternary encoding to generalise over the environmental
                 inputs and to associate appropriate actions. However, a
                 number of schemes have been presented beyond this,
                 ranging from integers to artificial neural networks.
                 This thesis investigates the use of Dynamical Genetic
                 Programming (DGP) as a knowledge representation within
                 LCS. DGP is a temporally dynamic, graph-based, symbolic
                 representation. Temporal dynamism has been identified
                 as an important aspect in biological systems,
                 artificial life, and cognition in general. Furthermore,
                 discrete dynamical systems have been found to exhibit
                 inherent content-addressable memory. In this thesis,
                 the collective emergent behaviour of ensembles of such
                 dynamical function networks are herein shown to be
                 exploitable toward solving various computational tasks.
                 Significantly, it is shown possible to exploit the
                 variable-length, adaptive memory existing inherently
                 within the networks under an asynchronous scheme, and
                 where all new parameters introduced are self-adaptive.
                 It is shown possible to exploit the collective
                 mechanics to solve both discrete and continuous-valued
                 reinforcement learning problems, and to perform
                 symbolic regression. In particular, the representation
                 is shown to provide improved performance beyond a
                 traditional Genetic Programming benchmark on a number
                 of a composite polynomial regression tasks. Superior
                 performance to previously published techniques is also
                 shown in a continuous-input-output reinforcement
                 learning problem. Finally, it is shown possible to
                 perform multi-step-ahead predictions of a financial
                 time-series by repeatedly sampling the network states
                 at succeeding temporal intervals",
  notes =        "uk.bl.ethos.557835",
}

Genetic Programming entries for Richard Preen

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