A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms
Created by W.Langdon from
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- @InProceedings{eurogp07:hemberg,
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author = "Erik Hemberg and Conor Gilligan and
Michael O'Neill and Anthony Brabazon",
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title = "A Grammatical Genetic Programming Approach to
Modularity in Genetic Algorithms",
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editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
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booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "4445",
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year = "2007",
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address = "Valencia, Spain",
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month = "11-13 " # apr,
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pages = "1--11",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-71602-5",
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isbn13 = "978-3-540-71602-0",
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DOI = "doi:10.1007/978-3-540-71605-1_1",
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abstract = "The ability of Genetic Programming to scale to
problems of increasing difficulty operates on the
premise that it is possible to capture regularities
that exist in a problem environment by decomposition of
the problem into a hierarchy of modules. As computer
scientists and more generally as humans we tend to
adopt a similar divide-and-conquer strategy in our
problem solving. In this paper we consider the adoption
of such a strategy for Genetic Algorithms. By adopting
a modular representation in a Genetic Algorithm we can
make efficiency gains that enable superior scaling
characteristics to problems of increasing size. We
present a comparison of two modular Genetic Algorithms,
one of which is a Grammatical Genetic Programming
algorithm, the meta-Grammar Genetic Algorithm (mGGA),
which generates binary string sentences instead of
traditional GP trees. A number of problems instances
are tackled which extend the Checkerboard problem by
introducing different kinds of regularity and noise.
The results demonstrate some limitations of the modular
GA (MGA) representation and how the mGGA can overcome
these. The mGGA shows improved scaling when compared
the MGA.",
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notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
- }
Genetic Programming entries for
Erik Hemberg
Conor Gilligan
Michael O'Neill
Anthony Brabazon
Citations