Econometric Genetic Programming Outperforms Traditional Econometric Algorithms for Regression Tasks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Novaes:2017:GECCO,
  author =       "Andre Luiz Farias Novaes and Ricardo Tanscheit and 
                 Douglas Mota Dias",
  title =        "Econometric Genetic Programming Outperforms
                 Traditional Econometric Algorithms for Regression
                 Tasks",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1427--1430",
  size =         "4 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3082506",
  DOI =          "doi:10.1145/3067695.3082506",
  acmid =        "3082506",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, feature
                 selection, model selection, multiple regression",
  month =        "15-19 " # jul,
  notes =        "Also known as \cite{Novaes:2017:EGP:3067695.3082506}
                 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 Andre Luiz Farias Novaes Ricardo Tanscheit Douglas Mota Dias

Citations