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GAReg  

Genetic Algorithms in Regression
View on CRAN: Click here


Download and install GAReg package within the R console
Install from CRAN:
install.packages("GAReg")

Install from Github:
library("remotes")
install_github("cran/GAReg")

Install by package version:
library("remotes")
install_version("GAReg", "0.1.0")



Attach the package and use:
library("GAReg")
Maintained by
Mo Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-02-09
Latest Update: 2026-02-09
Description:
Provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation is built on the 'GA' engine of Scrucca (2017, <doi:10.32614/RJ-2017-008>) and 'changepointGA' engine from Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.
How to cite:
Mo Li (2026). GAReg: Genetic Algorithms in Regression. R package version 0.1.0, https://cran.r-project.org/web/packages/GAReg. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.1.0 (2026-02-09 20:20), 0.1.1 (2026-03-29 16:10)
Other packages that cited GAReg R package
View GAReg citation profile
Other R packages that GAReg depends, imports, suggests or enhances
Complete documentation for GAReg
Functions, R codes and Examples using the GAReg R package
Full GAReg package functions and examples
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