Genetic Algorithms in Regression R package GAReg version 0.1.2 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. Software Depends: R (>= 4.3.0) Imports: stats, splines, utils, methods, changepointGA, GA Suggests: MASS, knitr, rmarkdown Mo Li <mo.li@louisiana.edu> Comprehensive R Archive Network (CRAN) Mo Li [aut, cre], QiQi Lu [aut], Robert Lund [aut], Xueheng Shi [aut] Apache License (== 2.0) 2026-04-27 application/tgz https://CRAN.R-project.org/package=GAReg doi:10.32614/CRAN.package.GAReg