Register a Genetic Algorithm Seriation Method

Register a GA-based seriation metaheuristic for use with seriate().

Usage

register_GA()

gaperm_mixedMutation(ismProb = 0.8)

Arguments

ismProb

probability to use GA::gaperm_ismMutation() (inversion) versus GA::gaperm_simMutation() (simple insertion).

Value

Nothing.

Details

Registers the method "GA" for seriate(). This method can be used to optimize any criterion in package seriation.

The GA uses by default the ordered cross-over (OX) operator. For mutation, the GA uses a mixture of simple insertion and simple inversion operators. This mixed operator is created using seriation::gaperm_mixedMutation(ismProb = .8), where ismProb is the probability that the simple insertion mutation operator is used. See package GA for a description of other available cross-over and mutation operators for permutations. The appropriate operator functions in GA start with gaperm_.

We warm start the GA using "suggestions" given by several heuristics. Set "suggestions" to NA to start with a purely random initial population.

See Example section for available control parameters.

Note: Package GA needs to be installed.

References

Luca Scrucca (2013): GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1–37. URL doi:10.18637/jss.v053.i04 .

See also

Other seriation: register_DendSer(), register_optics(), register_smacof(), register_tsne(), register_umap(), register_vegan(), registry_for_seriation_methods, seriate(), seriate_best()

Author

Michael Hahsler

Examples

if (FALSE) { # \dontrun{
register_GA()
get_seriation_method("dist", "GA")

data(SupremeCourt)
d <- as.dist(SupremeCourt)

## optimize for linear seriation criterion (LS)
o <- seriate(d, "GA", criterion = "LS", verbose = TRUE)
pimage(d, o)

## Note that by default the algorithm is already seeded with a LS heuristic.
## This run is no warm start (no suggestions) and increase run to 100
o <- seriate(d, "GA", criterion = "LS", suggestions = NA, run = 100,
  verbose = TRUE)
pimage(d, o)

o <- seriate(d, "GA", criterion = "LS", suggestions = NA, run = 100,
  verbose = TRUE,  )

pimage(d, o)
} # }