Programmatic Boosting

author = "Moshe Sipper and Jason H. Moore",

title = "Programmatic Boosting",

howpublished = "online",

year = "2020",

month = "10 " # aug,

note = "Apr 2023 Withdrawn, see instead \cite{Sipper_Moore:GPEM} \cite{Sipper:2022:mlwa}",

keywords = "genetic algorithms, genetic programming",

broken = "https://drive.google.com/file/d/1FbEh6_64f9PR7eC77NAAD5IRcCFGCXt_/view",

size = "9 pages",

abstract = "We modify standard gradient boosting by replacing the embedded weak learner in favour of a stronger one, a stochastically derived symbolic regressor. We call our technique programmatic boosting. Testing the merits of our new method by comparing it to 10 other algorithms over 98 datasets, we find that programmatic boosting is able to outperform all others in a significant number of cases.",

notes = "Sun, Aug 16, 2020 at 1:17 AM to GeneticProgramming@groups.io Gradient boosting and GP symbolic regression = Programmatic Boosting.

Jan 2021 drive.google.com gives in trash error See nov_2020_url instead (Apr 2023 also gone)

Uses the PMLB repository \cite{Orzechowski:2018:GECCO}

Symbolic Regressor, Kernel Ridge, AdaBoost Regressor, Gradient Boosting Regressor, Lasso Lars, Linear Regression, Random Forest Regressor, SGD Regressor, Linear SVR, XGB Regressor",