# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "GAReg" in publications use:' type: software license: Apache-2.0 title: 'GAReg: Genetic Algorithms in Regression' version: 0.1.2 doi: 10.32614/CRAN.package.GAReg abstract: 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, ). The computation is built on the 'GA' engine of Scrucca (2017, ) and 'changepointGA' engine from Li and Lu (2024, ). In challenging high-dimensional settings, 'GAReg' enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified. authors: - family-names: Li given-names: Mo email: mo.li@louisiana.edu - family-names: Lu given-names: QiQi email: qlu2@vcu.edu - family-names: Lund given-names: Robert email: rolund@ucsc.edu - family-names: Shi given-names: Xueheng email: shixueheng@gmail.com repository: https://mli171.r-universe.dev repository-code: https://github.com/mli171/GAReg commit: 4c91d47b78ed3b660581652d6e8203039d897101 url: https://github.com/mli171/GAReg date-released: '2026-04-27' contact: - family-names: Li given-names: Mo email: mo.li@louisiana.edu