Structural difficulty in grammatical evolution versus genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Thorhauer:2013:GECCO,
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author = "Ann Thorhauer and Franz Rothlauf",
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title = "Structural difficulty in grammatical evolution versus
genetic programming",
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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 = "997--1004",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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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:10.1145/2463372.2463491",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Genetic programming (GP) has problems with structural
difficulty as it is unable to search effectively for
solutions requiring very full or very narrow trees. As
a result of structural difficulty, GP has a bias
towards narrow trees which means it searches
effectively for solutions requiring narrow trees. This
paper focuses on the structural difficulty of
grammatical evolution (GE). In contrast to GP, GE works
on variable-length binary strings and uses a grammar in
Backus-Naur Form (BNF) to map linear genotypes to
phenotype trees. The paper studies whether and how GE
is affected by structural difficulty. For the analysis,
we perform random walks through the search space and
compare the structure of the visited solutions. In
addition, we compare the performance of GE and GP for
the Lid problem. Results show that GE representation is
biased, this means it has problems with structural
difficulty. For binary trees, GE has a bias towards
narrow and deep structures; thus GE outperforms
standard GP if optimal solutions are composed of very
narrow and deep structures. In contrast, problems where
optimal solutions require more dense trees are easier
to solve for GP than for GE.",
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notes = "Also known as \cite{2463491} 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
Ann Thorhauer
Franz Rothlauf
Citations