# An improved $\lambda$-linear genetic programming evaluated in solving the Santa Fe ant trail problem

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@InProceedings{conf/sac/SottoMB16,
• author = "Leo Francoso Dal Piccol Sotto and Vinicius Veloso {de Melo} and Marcio P. Basgalupp",
• title = "An improved {$\lambda$}-linear genetic programming evaluated in solving the Santa Fe ant trail problem",
• booktitle = "Proceedings of the 31st Annual {ACM} Symposium on Applied Computing",
• publisher = "ACM",
• year = "2016",
• editor = "Sascha Ossowski",
• month = apr # " 4-8",
• pages = "103--108",
• isbn13 = "978-1-4503-3739-7",
• keywords = "genetic algorithms, genetic programming, linear genetic programming, santa fe ant trail",
• bibdate = "2016-06-07",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2016.html#SottoMB16",
• URL = "http://doi.acm.org/10.1145/2851613",
• DOI = "doi:10.1145/2851613.2851669",
• acmid = "2851669",
• size = "6 pages",
• 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