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
changepointGA: Genetic Algorithms for Changepoint Models
Comparing multiple changepoint configurations by pairwise distance
S4 Class Definition for cptga
Genetic algorithm
S4 Class Definition for cptgaisl
Island model based genetic algorithm
The default mutation operator in genetic algorithm
Plot Time Series with Detected Changepoints from a cptga Object
Plot Time Series with Detected Changepoints from a cptgaisl Object
Print Summary for a cptga Object
Print Summary for a cptgaisl Object
Random population initialization
The default parents selection genetic algorithm operator
Randomly select the chromosome
Time series simulation with changepoint effects
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>).