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  "Title": "Changepoint Detection via Modified Genetic Algorithms",
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  "Date": "2026-05-18",
  "Authors@R": "c(\nperson(given = \"Mo\",\nfamily = \"Li\",\nrole = c(\"aut\", \"cre\"),\nemail = \"mo.li@louisiana.edu\"),\nperson(given = \"QiQi\",\nfamily = \"Lu\",\nrole = \"aut\",\nemail = \"qlu2@vcu.edu\")\n)",
  "Description": "The Genetic Algorithm (GA) is used to perform changepoint\nanalysis in time series data. The package also includes an\nextended island version of GA, as described in Lu, Lund, and\nLee (2010, <doi:10.1214/09-AOAS289>). By mimicking the\nprinciples of natural selection and evolution, GA provides a\npowerful stochastic search technique for solving combinatorial\noptimization problems. In 'changepointGA', each chromosome\nrepresents a changepoint configuration, including the number\nand locations of changepoints, hyperparameters, and model\nparameters. The package employs genetic operators—selection,\ncrossover, and mutation—to iteratively improve solutions based\non the given fitness (objective) function. Key features of\n'changepointGA' include encoding changepoint configurations in\nan integer format, enabling dynamic and simultaneous estimation\nof model hyperparameters, changepoint configurations, and\nassociated parameters. The detailed algorithmic implementation\ncan be found in the package vignettes and in the paper of Li\n(2024, <doi:10.48550/arXiv.2410.15571>).",
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}