Econometric genetic programming outperforms traditional econometric algorithms for regression tasks

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.