Econometric Genetic Programming Outperforms Traditional Econometric Algorithms for Regression Tasks

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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,
• 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

Citations