Abstract:Detecting changepoints in a time series of length $N$ entails evaluating up to $2^{N-1}$ possible changepoint models, making exhaustive enumeration computationally infeasible. Genetic algorithms (GAs) provide a stochastic way to identify the structural changes: a population of candidate models evolves via selection, crossover, and mutation operators until it converges on one changepoint model that balances the goodness-of-fit with parsimony. The R package changepointGA encodes each candidate model as an integer chromosome vector and supports both the basic single-population model GA and the island model GA. Parallel computing is implemented on multi-core hardware to further accelerate computation. Users may supply custom fitness functions or genetic operators, while a user-friendly wrapper streamlines routine analyses. Extensive simulations demonstrate that our package runs significantly faster than binary-encoded GA alternatives. Additionally, this package can simultaneously locate changepoints and estimate their effects, as well as other model parameters and any integer-valued hyperparameters. Applications to array-based comparative genomic hybridization data and a century-long temperature series further highlight the package's value in biological and climate research.
| Subjects: | Computation (stat.CO); Applications (stat.AP) |
| Cite as: | arXiv:2410.15571 [stat.CO] |
| (or arXiv:2410.15571v3 [stat.CO] for this version) | |
| https://doi.org/10.48550/arXiv.2410.15571 arXiv-issued DOI via DataCite |
Submission history
From: Mo Li [view email]
[v1]
Mon, 21 Oct 2024 01:34:54 UTC (5,421 KB)
[v2]
Mon, 29 Sep 2025 14:56:03 UTC (1,453 KB)
[v3]
Fri, 19 Jun 2026 01:53:22 UTC (1,451 KB)