mutation: The default mutation operator in genetic algorithm
In changepointGA: Changepoint Detection via Modified Genetic Algorithm

mutation

R Documentation

The default mutation operator in genetic algorithm

Description

In a certain probability, the mutation genetic operator can be applied to generate a new child. By default, the new child selection can be down by the similar individual selection method in population initialization, selectTau.

Usage

mutation(child, prange = NULL, minDist, pchangepoint, lmax, mmax, N)

Arguments

`child`	The child chromosome resulting from the `crossover` genetic operator.
`prange`	Default is `NULL` for only changepoint detection. If `prange` is specified as a list object, which contains the range of each model order parameters for order selection (integers). The number of order parameters must be equal to the length of `prange`.
`minDist`	The required minimum distance between two adjacent changepoints.
`pchangepoint`	The probability of changepoints for every time series.
`lmax`	The user specified maximum number of changepoints, by default, as `N/2 - 1`.
`mmax`	The user specified maximum length of individual chromosome, by default, as `2+N/2 + 1`.
`N`	The sample size of the time series.

Details

A function can apply mutation to the produced child with the specified probability pmutation in cptga and cptgaisl. If order selection is not requested (option = "cp" in cptga and cptgaisl), the default mutation operator function uses selectTau to select a completely new individual with a new chromosome as the mutated child. For details, see selectTau. If order selection is needed (option = "both" in cptga and cptgaisl), we first decide whether to keep the produced child's model order with a probability of 0.5. If the child's model order is retained, the selectTau function is used to select a completely new individual with a new chromosome as the mutated child. If a new model order is selected from the candidate model order set, there is a 0.5 probability to either select a completely new individual with new changepoint locations or retain the original child's changepoint locations for the mutated child. Note that the current model orders in the child's chromosome are excluded from the set to avoid redundant objective function evaluation. Finally, the function returns a vector containing the modified chromosomes for mutated child.