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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling source/ R- | GAReg_0.1.1.tar.gz |
128.8 KiB |
0.1.1 |
rolling linux/jammy R-4.5 | GAReg_0.1.1.tar.gz |
342.9 KiB |
0.1.1 |
latest source/ R- | GAReg_0.1.1.tar.gz |
128.8 KiB |
0.1.1 |
latest linux/jammy R-4.5 | GAReg_0.1.1.tar.gz |
342.9 KiB |
0.1.1 |
2026-04-23 source/ R- | GAReg_0.1.1.tar.gz |
128.8 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | GAReg_0.1.1.zip |
345.0 KiB |