Comparison of two methods for computing action values in XCS with code-fragment actions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Iqbal:2013:GECCOcomp,
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author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
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title = "Comparison of two methods for computing action values
in XCS with code-fragment actions",
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booktitle = "GECCO '13 Companion: Proceeding of the fifteenth
annual conference companion on Genetic and evolutionary
computation conference companion",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and
Thomas Bartz-Beielstein and Daniele Loiacono and
Francisco Luna and Joern Mehnen and Gabriela Ochoa and
Mike Preuss and Emilia Tantar and Leonardo Vanneschi and
Kent McClymont and Ed Keedwell and Emma Hart and
Kevin Sim and Steven Gustafson and
Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Heike Trautmann and Muhammad Iqbal and Kamran Shafi and
Ryan Urbanowicz and Stefan Wagner and
Michael Affenzeller and David Walker and Richard Everson and
Jonathan Fieldsend and Forrest Stonedahl and
William Rand and Stephen L. Smith and Stefano Cagnoni and
Robert M. Patton and Gisele L. Pappa and
John Woodward and Jerry Swan and Krzysztof Krawiec and
Alexandru-Adrian Tantar and Peter A. N. Bosman and
Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and
David L. Gonzalez-Alvarez and
Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and
Kenneth Holladay and Tea Tusar and Boris Naujoks",
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isbn13 = "978-1-4503-1964-5",
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keywords = "genetic algorithms, genetic programming",
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pages = "1235--1242",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2464576.2482702",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "XCS is a learning classifier system that uses
accuracy-based fitness to learn a problem. Commonly, a
classifier rule in XCS is encoded using a ternary
alphabet based condition and a numeric action.
Previously, we implemented a code-fragment action based
XCS, called XCSCFA, where the typically used numeric
action was replaced by a genetic programming like
tree-expression. In XCSCFA, the action value in a
classifier was computed by loading the terminal symbols
in the action-tree with the corresponding binary values
in the condition of the classifier rule. This enabled
accurate, general and compact rule sets to be simply
produced. The main contribution of this work is to
investigate an intuitive way, i.e. using the
environmental instance, to compute the action value in
XCSCFA, instead of the condition of the classifier
rule. The methods will be compared in five different
Boolean problem domains, i.e. multiplexer, even-parity,
majority-on, design verification, and carry problems.
The environmental instance based XCSCFA approach had
better classification performance than standard XCS as
well as classifier condition based XCSCFA and solved
all the problems experimented here. In addition it
produced more general and compact classifier rules in
the final solution. However, classifier condition based
XCSCFA has the advantage of producing the optimal
classifiers such that they are clearly separated from
the sub-optimal ones in certain domains.",
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notes = "Also known as \cite{2482702} Distributed at
GECCO-2013.",
- }
Genetic Programming entries for
Muhammad Iqbal
Will N Browne
Mengjie Zhang
Citations