Comparison of two methods for computing action values in XCS with code-fragment actions

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
  title =        "Comparison of two methods for computing action values
                 in XCS with code-fragment actions",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  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",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1235--1242",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2482702",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  notes =        "Also known as \cite{2482702} Distributed at

Genetic Programming entries for Muhammad Iqbal Will N Browne Mengjie Zhang