Dynamical genetic programming in learning classifier systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8154
- @PhdThesis{Preen:thesis,
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author = "Richard John Preen",
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title = "Dynamical genetic programming in learning classifier
systems",
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school = "University of the West of England",
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year = "2011",
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address = "Bristol, UK",
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keywords = "genetic algorithms, genetic programming, artificial
genetic regulatory networks, knowledge representation,
learning classifer systems, xcs, xcsf",
-
URL = "http://eprints.uwe.ac.uk/25852/",
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URL = "http://ethos.bl.uk/OrderDetails.do?did=31&uin=uk.bl.ethos.557835",
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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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