Set crossover of turbine layouts

Combine two layouts of n unique grid-cell IDs. Shared sites are kept; remaining sites are sampled from the parents' exclusive cells and, with rate p_inject, from grid cells that neither parent uses. Identical parents still get unused cells injected so the search does not freeze. Every child has exactly n turbines.

Usage

set_crossover(
  ids,
  grid_ids,
  uplimit = 300,
  seed = NULL,
  verbose = FALSE,
  p_inject = NULL,
  grid_xy = NULL,
  visit = NULL,
  p_spatial = NULL
)

Arguments

ids

Integer matrix with n rows (turbines) and one column per parent

grid_ids

All valid grid cell IDs

uplimit

Maximum number of children. Default is 300

seed

Set a seed for comparability. Default is NULL

verbose

If TRUE, print the number of children

p_inject

Fraction of non-shared slots filled from unused grid cells. Default is getOption("windfarmGA.crossover_inject") (0.25). At least one unused cell is injected when any are available.

grid_xy

Optional matrix/data.frame with columns ID, X, Y. If given, a spatial half-plane crossover is used with probability p_spatial.

visit

Named visit counts per grid ID (undersampled cells preferred)

p_spatial

Probability of spatial (vs set) crossover when grid_xy is given. Default is getOption("windfarmGA.spatial_crossover") (0.5)

Value

Integer matrix of unique grid IDs (n x children)

See also

Other Genetic Algorithm Functions: crossover(), fitness(), genetic_algorithm(), init_population(), mutation(), selection(), swap_mutation(), trimton()

Examples

ids <- cbind(c(1, 3, 5, 7), c(1, 4, 5, 9))
set_crossover(ids, grid_ids = 1:20, uplimit = 4, seed = 1)
#>      [,1] [,2] [,3] [,4]
#> [1,]    1    1    1    1
#> [2,]    2    5    4    5
#> [3,]    4    9    5    6
#> [4,]    5   19   17    9