Dynamical genetic programming in learning classifier systems

Created by W.Langdon from gp-bibliography.bib Revision:1.8187

@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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