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.