# mutation: Mutation Method In windfarmGA: Genetic Algorithm for Wind Farm Layout Optimization

## Description

The function randomly mutates an individual's genetic code

## Usage

 `1` ```mutation(a, p, seed = NULL) ```

## Arguments

 `a` The binary matrix of all individuals `p` The mutation rate `seed` Set a seed for comparability. Default is `NULL`

## Value

Returns a binary matrix with mutated genes.

Other Genetic Algorithm Functions: `crossover()`, `fitness()`, `genetic_algorithm()`, `init_population()`, `selection()`, `trimton()`, `windfarmGA()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```## Create 4 random individuals with binary values a <- cbind(bin0 = sample(c(0,1), 20, replace=TRUE, prob = c(70,30)), bin1 = sample(c(0,1), 20, replace=TRUE, prob = c(30,70)), bin2 = sample(c(0,1), 20, replace=TRUE, prob = c(30,70)), bin3 = sample(c(0,1), 20, replace=TRUE, prob = c(30,70))) a ## Mutate the individuals with a low percentage aMut <- mutation(a, 0.1, NULL) ## Check which values are not like the originals a == aMut ## Mutate the individuals with a high percentage aMut <- mutation(a, 0.4, NULL) ## Check which values are not like the originals a == aMut ```