Extending learning classifier system with cyclic graphs for scalability on complex, large-scale Boolean problems
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gp-bibliography.bib Revision:1.7970
- @InProceedings{Iqbal:2013:GECCO,
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author = "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
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title = "Extending learning classifier system with cyclic
graphs for scalability on complex, large-scale
{Boolean} problems",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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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",
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isbn13 = "978-1-4503-1963-8",
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pages = "1045--1052",
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keywords = "genetic algorithms, genetic programming",
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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:10.1145/2463372.2463500",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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