changepointGA - Changepoint Detection via Modified Genetic Algorithms
The Genetic Algorithm (GA) is used to perform changepoint
analysis in time series data. The package also includes an
extended island version of GA, as described in Lu, Lund, and
Lee (2010, <doi:10.1214/09-AOAS289>). By mimicking the
principles of natural selection and evolution, GA provides a
powerful stochastic search technique for solving combinatorial
optimization problems. In 'changepointGA', each chromosome
represents a changepoint configuration, including the number
and locations of changepoints, hyperparameters, and model
parameters. The package employs genetic operators—selection,
crossover, and mutation—to iteratively improve solutions based
on the given fitness (objective) function. Key features of
'changepointGA' include encoding changepoint configurations in
an integer format, enabling dynamic and simultaneous estimation
of model hyperparameters, changepoint configurations, and
associated parameters. The detailed algorithmic implementation
can be found in the package vignettes and in the paper of Li
(2024, <doi:10.48550/arXiv.2410.15571>).
