Constituent Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Georgiou:2011:IJCAI,
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author = "Loukas Georgiou and William J. Teahan",
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title = "Constituent Grammatical Evolution",
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booktitle = "Proceedings of the Twenty-Second International Joint
Conference on Artificial Intelligence",
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year = "2011",
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editor = "Toby Walsh",
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pages = "1261--1268",
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address = "Barcelona, Spain",
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publisher_address = "Menlo Park, California, USA",
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month = "16-22 " # jul,
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organisation = "International Joint Conferences on Artificial
Intelligence",
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publisher = "AAAI Press",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Santa Fe Trail",
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isbn13 = "978-1-57735-512-0",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.1709",
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URL = "http://ijcai.org/papers11/Papers/IJCAI11-214.pdf",
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size = "8 pages",
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abstract = "We present Constituent Grammatical Evolution (CGE), a
new evolutionary automatic programming algorithm that
extends the standard Grammatical Evolution algorithm by
incorporating the concepts of constituent genes and
conditional behaviour-switching. CGE builds from
elementary and more complex building blocks a control
program which dictates the behaviour of an agent and it
is applicable to the class of problems where the
subject of search is the behaviour of an agent in a
given environment. It takes advantage of the powerful
Grammatical Evolution feature of using a BNF grammar
definition as a plug-in component to describe the
output language to be produced by the system. The main
benchmark problem in which CGE is evaluated is the
Santa Fe Trail problem using a BNF grammar definition
which defines a search space semantically equivalent
with that of the original definition of the problem by
Koza. Furthermore, CGE is evaluated on two additional
problems, the Los Altos Hills and the Hampton Court
Maze. The experimental results demonstrate that
Constituent Grammatical Evolution outperforms the
standard Grammatical Evolution algorithm in these
problems, in terms of both efficiency (percent of
solutions found) and effectiveness (number of required
steps of solutions found).",
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notes = "Santa Fe Ant, Lost Altos Hills, Hampton Court Maze,
jGE http://ijcai.org/papers11/contents.php",
- }
Genetic Programming entries for
Loukas Georgiou
William J Teahan
Citations