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

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.

See Also

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

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")

## 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")

windfarmGA documentation built on May 5, 2021, 5:08 p.m.