Changepoint Detection via Modified Genetic Algorithm
Average crossover operator to produce offspring for AMOC problem
Jump mutation operator to produce offspring for AMOC problem
Random population initialization for AMOC problem
The parents selection genetic algorithm operator for AMOC problem
Calculating BIC for Multiple changepoint detection with model order se...
Example function: Calculating BIC for AR(1) model
Comparing multiple changepoint configurations by pairwise distance
GA_param
Genetic algorithm
IslandGA_param
Island model based genetic algorithm
The default mutation operator in genetic algorithm
operators
Random population initialization
The default parents selection genetic algorithm operator
Randomly select the chromosome
Time series simulation with changepoint effects
Plot the simulated time series
Uniform crossover to produce offsprings
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>).