# crossover: Crossover Method In windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization

## Description

The crossover method creates new offspring with the selected individuals by permutating their genetic codes.

## Usage

 `1` ```crossover(se6, u, uplimit, crossPart, 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.

Other Genetic Algorithm Functions: `fitness()`, `genetic_algorithm()`, `init_population()`, `mutation()`, `selection()`, `trimton()`, `windfarmGA()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## 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") ## with 3 crossover parts and equal locations crossover(CrossSampl, u = 2.5, uplimit = 300, crossPart = "EQU") ## or with random locations and 5 crossover parts crossover(CrossSampl, u = 4.9, uplimit = 300, crossPart = "RAN") ```