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  "Title": "Changepoint Detection via Modified Genetic Algorithms",
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  "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. The structured and\nconsensus-guided island model GA additionally incorporates\ncross-island consensus guidance and optional objective-based\nlocal refinement. Key features of 'changepointGA' include\nencoding changepoint configurations in an integer format,\nenabling dynamic and simultaneous estimation of model\nhyperparameters, changepoint configurations, and associated\nparameters. The detailed algorithmic implementation can be\nfound in the package vignettes and in the paper of Li and Lu\n(2024, <doi:10.48550/arXiv.2410.15571>).",
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