This function performs crossover between the selected individuals that fit the best
based on the predefined condition(aim/objective).

e.g.: To optimize the function \(f(x) = x^2 - 4x + 4\)
to find the value of \(x\) that minimizes the function.
\(x\): represents a possible value the an individual from the population can have.

## Arguments

- selected_parents
The list of selected individuals from the population.

- offspring_size
The number of offspring that the selected population should have.

## Examples

```
population <- c(1, 3, 0)
# Evaluate fitness
fitness <- evaluate_fitness(population)
print("Evaluation:")
#> [1] "Evaluation:"
print(fitness)
#> [1] 1 1 4
# Selection
selected_parents <- selection(population, fitness, num_parents = 2)
print("Selection:")
#> [1] "Selection:"
print(selected_parents)
#> [1] 1 3
# Crossover
offspring <- crossover(selected_parents, offspring_size = 2)
print("Crossover:")
#> [1] "Crossover:"
print(offspring)
#> [1] 1 1
```