GAReg: Genetic Algorithms in Regression
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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.3.0) |
| Imports: |
stats, splines, utils, methods, changepointGA, GA |
| Suggests: |
MASS, knitr, rmarkdown |
| Published: |
2026-02-09 |
| DOI: |
10.32614/CRAN.package.GAReg (may not be active yet) |
| Author: |
Mo Li [aut, cre],
QiQi Lu [aut],
Robert Lund [aut],
Xueheng Shi [aut] |
| Maintainer: |
Mo Li <mo.li at louisiana.edu> |
| BugReports: |
https://github.com/mli171/GAReg/issues |
| License: |
Apache License (== 2.0) |
| URL: |
https://github.com/mli171/GAReg |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
GAReg results |
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