The crossover method creates new offspring with the selected individuals by permutating their genetic codes.
Usage
crossover(se6, u, uplimit, crossPart = c("EQU", "RAN"), verbose, seed)Arguments
- se6
The selected individuals. The output of
selection- u
The crossover point rate
- uplimit
The upper limit of allowed permutations
- crossPart
The crossover method. Either "EQU" or "RAN"
- verbose
If
TRUE, will print out further information- seed
Set a seed for comparability. Default is
NULL
Value
Returns a binary coded matrix of all permutations and all grid cells, where 0 indicates no turbine and 1 indicates a turbine in the grid cell.
See also
Other Genetic Algorithm Functions:
fitness(),
genetic_algorithm(),
init_population(),
mutation(),
selection(),
trimton()
Examples
## Create two random parents with an index and random binary values
Parents <- data.frame(
ID = 1:20,
bin = sample(c(0, 1), 20, replace = TRUE, prob = c(70, 30)),
bin.1 = sample(c(0, 1), 20, replace = TRUE, prob = c(30, 70))
)
## Create random Fitness values for both individuals
FitParents <- data.frame(ID = 1, Fitness = 1000, Fitness.1 = 20)
## Assign both values to a list
CrossSampl <- list(Parents, FitParents)
## Cross their data at equal locations with 2 crossover parts
crossover(CrossSampl, u = 1.1, uplimit = 300, crossPart = "EQU")
#> [,1] [,2] [,3] [,4]
#> [1,] 1 1 0 0
#> [2,] 1 1 0 0
#> [3,] 1 1 0 0
#> [4,] 1 1 0 0
#> [5,] 1 1 0 0
#> [6,] 0 0 1 1
#> [7,] 0 0 1 1
#> [8,] 1 1 0 0
#> [9,] 1 1 0 0
#> [10,] 0 0 0 0
#> [11,] 0 0 0 0
#> [12,] 0 0 0 0
#> [13,] 1 0 1 0
#> [14,] 1 1 1 1
#> [15,] 1 0 1 0
#> [16,] 0 0 0 0
#> [17,] 0 1 0 1
#> [18,] 0 1 0 1
#> [19,] 1 1 1 1
#> [20,] 1 0 1 0
## with 3 crossover parts and equal locations
crossover(CrossSampl, u = 2.5, uplimit = 300, crossPart = "EQU")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 1 1 1 1 0 0 0 0
#> [2,] 1 1 1 1 0 0 0 0
#> [3,] 1 1 1 1 0 0 0 0
#> [4,] 1 1 1 1 0 0 0 0
#> [5,] 1 1 1 1 0 0 0 0
#> [6,] 0 0 0 0 1 1 1 1
#> [7,] 0 0 0 0 1 1 1 1
#> [8,] 1 1 0 0 1 1 0 0
#> [9,] 1 1 0 0 1 1 0 0
#> [10,] 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0
#> [13,] 1 1 0 0 1 1 0 0
#> [14,] 1 1 1 1 1 1 1 1
#> [15,] 1 0 1 0 1 0 1 0
#> [16,] 0 0 0 0 0 0 0 0
#> [17,] 0 1 0 1 0 1 0 1
#> [18,] 0 1 0 1 0 1 0 1
#> [19,] 1 1 1 1 1 1 1 1
#> [20,] 1 0 1 0 1 0 1 0
## or with random locations and 5 crossover parts
crossover(CrossSampl, u = 4.9, uplimit = 300, crossPart = "RAN")
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [4,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [5,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [6,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [9,] 1 1 1 1 1 1 1 1 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 1 1 1 1 1 1 1 1 0 0 0 0 0
#> [14,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [15,] 1 1 1 1 0 0 0 0 1 1 1 1 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 1 1 0 0 1 1 0 0 1 1 0
#> [18,] 0 1 0 1 0 1 0 1 0 1 0 1 0
#> [19,] 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [20,] 1 0 1 0 1 0 1 0 1 0 1 0 1
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 1 1 1 0 0 0 0 0 0 0 0 0
#> [2,] 1 1 1 0 0 0 0 0 0 0 0 0
#> [3,] 1 1 1 0 0 0 0 0 0 0 0 0
#> [4,] 1 1 1 0 0 0 0 0 0 0 0 0
#> [5,] 1 1 1 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 1 1 1 1 1 1 1 1 1
#> [7,] 0 0 0 1 1 1 1 1 1 1 1 1
#> [8,] 1 1 1 0 0 0 0 0 0 0 0 0
#> [9,] 0 0 0 1 1 1 1 1 1 1 1 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 0 1 1 1 1 1 1 1 1 0
#> [14,] 1 1 1 1 1 1 1 1 1 1 1 1
#> [15,] 0 0 0 1 1 1 1 0 0 0 0 1
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 1 1 0 0 1 1 0 0 1 1 0
#> [18,] 1 0 1 0 1 0 1 0 1 0 1 0
#> [19,] 1 1 1 1 1 1 1 1 1 1 1 1
#> [20,] 0 1 0 1 0 1 0 1 0 1 0 1
#> [,26] [,27] [,28] [,29] [,30] [,31] [,32]
#> [1,] 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0
#> [6,] 1 1 1 1 1 1 1
#> [7,] 1 1 1 1 1 1 1
#> [8,] 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0 0
#> [14,] 1 1 1 1 1 1 1
#> [15,] 1 1 1 0 0 0 0
#> [16,] 0 0 0 0 0 0 0
#> [17,] 0 1 1 0 0 1 1
#> [18,] 1 0 1 0 1 0 1
#> [19,] 1 1 1 1 1 1 1
#> [20,] 0 1 0 1 0 1 0