A Probabilistic Linear Genetic Programming with Stochastic Context-free Grammar for Solving Symbolic Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

@InProceedings{Sotto:2017:GECCO,
  author =       "Leo Francoso Dal Piccol Sotto and 
                 Vinicius Veloso {de Melo}",
  title =        "A Probabilistic Linear Genetic Programming with
                 Stochastic Context-free Grammar for Solving Symbolic
                 Regression Problems",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "1017--1024",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071325",
  DOI =          "doi:10.1145/3071178.3071325",
  acmid =        "3071325",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, estimation of
                 distribution algorithms, linear genetic programming,
                 symbolic regression",
  month =        "15-19 " # jul,
  abstract =     "Traditional Linear Genetic Programming algorithms are
                 based only on the selection mechanism to guide the
                 search. Genetic operators combine or mutate random
                 portions of the individuals, without knowing if the
                 result will lead to a fitter individual. Probabilistic
                 Model Building Genetic Programming was proposed to
                 overcome this issue through a probability model that
                 captures the structure of the fit individuals and use
                 it to sample new individuals. This work proposes the
                 use of LGP with a Stochastic Context-Free Grammar, that
                 has a probability distribution that is updated
                 according to selected individuals. We proposed a method
                 for adapting the grammar into the linear representation
                 of LGP. Tests performed with the proposed probabilistic
                 method, and with two hybrid approaches, on several
                 symbolic regression benchmark problems show that the
                 results are statistically better than the obtained by
                 the traditional LGP.",
  notes =        "Also known as \cite{Sotto:2017:PLG:3071178.3071325}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",
}

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Vinicius Veloso de Melo

Citations