Extending learning classifier system with cyclic graphs for scalability on complex, large-scale Boolean problems

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@InProceedings{Iqbal:2013:GECCO,
  author =       "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
  title =        "Extending learning classifier system with cyclic
                 graphs for scalability on complex, large-scale
                 {Boolean} problems",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "1045--1052",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463500",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Evolutionary computational techniques have had limited
                 capabilities in solving large-scale problems, due to
                 the large search space demanding large memory and much
                 longer training time. Recently work has begun on
                 autonomously reusing learnt building blocks of
                 knowledge to scale from low dimensional problems to
                 large-scale ones. An XCS-based classifier system has
                 been shown to be scalable, through the addition of
                 tree-like code fragments, to a limit beyond standard
                 learning classifier systems. Self-modifying Cartesian
                 genetic programming (SMCGP) can provide general
                 solutions to a number of problems, but the obtained
                 solutions for large-scale problems are not easily
                 interpretable. A limitation in both techniques is the
                 lack of a cyclic representation, which is inherent in
                 finite state machines. Hence this work introduces a
                 state-machine based encoding scheme into scalable XCS,
                 for the first time, in an attempt to develop a general
                 scalable classifier system producing easily
                 interpretable classifier rules. The proposed system has
                 been tested on four different Boolean problem domains,
                 i.e. even-parity, majority-on, carry, and multiplexer
                 problems. The proposed approach outperformed standard
                 XCS in three of the four problem domains. In addition,
                 the evolved machines provide general solutions to the
                 even-parity and carry problems that are easily
                 interpretable as compared with the solutions obtained
                 using SMCGP.",
  notes =        "Also known as \cite{2463500} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",
}

Genetic Programming entries for Muhammad Iqbal Will N Browne Mengjie Zhang

Citations