Description Usage Arguments Value See Also Examples

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

1 | ```
crossover(se6, u, uplimit, crossPart, verbose, seed)
``` |

`se6` |
The selected individuals. The output of |

`u` |
The crossover point rate |

`uplimit` |
The upper limit of allowed permutations |

`crossPart` |
The crossover method. Either "EQU" or "RAN" |

`verbose` |
If |

`seed` |
Set a seed for comparability. Default is |

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")
``` |

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