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  "Title": "Genetic Algorithms in Regression",
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  "Authors@R": "c(\nperson(\ngiven = \"Mo\",\nfamily = \"Li\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mo.li@louisiana.edu\"),\nperson(\ngiven = \"QiQi\",\nfamily = \"Lu\",\nrole = \"aut\",\nemail = \"qlu2@vcu.edu\"),\nperson(\ngiven = \"Robert\",\nfamily = \"Lund\",\nrole = \"aut\",\nemail = \"rolund@ucsc.edu\"),\nperson(\ngiven = \"Xueheng\",\nfamily = \"Shi\",\nrole = \"aut\",\nemail = \"shixueheng@gmail.com\")\n)",
  "Description": "Provides a genetic algorithm framework for regression\nproblems requiring discrete optimization over model spaces with\nunknown or varying dimension, where gradient-based methods and\nexhaustive enumeration are impractical. Uses a compact\nchromosome representation for tasks including spline knot\nplacement and best-subset variable selection, with\nconstraint-preserving crossover and mutation, exact uniform\ninitialization under spacing constraints, steady-state\nreplacement, and optional island-model parallelization from Lu,\nLund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation\nis built on the 'GA' engine of Scrucca (2017,\n<doi:10.32614/RJ-2017-008>) and 'changepointGA' engine from Li\nand Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging\nhigh-dimensional settings, 'GAReg' enables efficient search and\ndelivers near-optimal solutions when alternative algorithms are\nnot well-justified.",
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      "title": "Genetic-Algorithm–based Optimal Knot Selection",
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      "topics": [
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