An improved $\lambda$-linear genetic programming evaluated in solving the Santa Fe ant trail problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{conf/sac/SottoMB16,
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author = "Leo Francoso Dal Piccol Sotto and
Vinicius Veloso {de Melo} and Marcio P. Basgalupp",
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title = "An improved {$\lambda$}-linear genetic programming
evaluated in solving the Santa Fe ant trail problem",
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booktitle = "Proceedings of the 31st Annual {ACM} Symposium on
Applied Computing",
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publisher = "ACM",
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year = "2016",
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editor = "Sascha Ossowski",
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address = "Pisa, Italy",
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month = apr # " 4-8",
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pages = "103--108",
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isbn13 = "978-1-4503-3739-7",
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keywords = "genetic algorithms, genetic programming, linear
genetic programming, santa fe ant trail",
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bibdate = "2016-06-07",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sac/sac2016.html#SottoMB16",
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URL = "http://doi.acm.org/10.1145/2851613",
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DOI = "doi:10.1145/2851613.2851669",
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acmid = "2851669",
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size = "6 pages",
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abstract = "We propose in this paper a new approach called
lambda-LGP (lambda-Linear Genetic Programming), a
variation of the well-know LGP (Linear Genetic
Programming) algorithm. Starting with an LGP based only
on effective macro and micromutations, the l-LGP
proposed in this work consists in extending the way in
which the individuals are chosen for reproduction. In
this model, a constant number (l) of a particular kind
of mutation is applied to each individual, thus
exploring its neighbouring fitness regions, and might
be replaced by one of its children according to
different criteria. Several configurations were tested
in the benchmark problem known as Santa Fe Ant Trail.
Results obtained show a very significant improvement by
using this proposed variation. For the Ant Trail
problem, lambda-LGP outperformed not only LGP, but also
several state-of-the-art methods.",
- }
Genetic Programming entries for
Leo Francoso Dal Piccol Sotto
Vinicius Veloso de Melo
Marcio Porto Basgalupp
Citations