Econometric Genetic Programming Outperforms Traditional Econometric Algorithms for Regression Tasks

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

  author =       "Andre Luiz Farias Novaes and Ricardo Tanscheit and 
                 Douglas Mota Dias",
  title =        "Econometric Genetic Programming Outperforms
                 Traditional Econometric Algorithms for Regression
  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 =          "",
  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,
  abstract =     "Econometric Genetic Programming (EGP) evolves multiple
                 linear regressions through Genetic Programming (GP),
                 which is responsible for model selection, aiming to
                 generate high accuracy regressions with potential
                 interpretability of parameters. It uses statistical
                 significance as a feature selection tool, directly and
                 efficiently identifying introns and controlling bloat.
                 In this paper, EGP is tested against traditional
                 feature-selection econometric algorithms in regression
                 tasks - namely Partial Least Squares Regression, Ridge
                 Regression and Stepwise Forward Regression -
                 outperforming them in all three datasets. The way EGP
                 explores search space of possible regressors and models
                 is crucial for its results. EGP is carefully
                 constructed considering econometric theory on
                 cross-sectional datasets, giving rigorous treatment on
                 topics like homoscedasticity and heteroscedasticity,
                 statistical inference for estimated parameters and
                 sampling criteria. It also benefits by the mathematical
                 proof on accuracy and statistical significance:
                 accuracy will only increase if the regressor presents a
                 test's statistics module in a two-sided hypothesis
                 testing higher than a predefined value.",
  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