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Showing content from https://danymukesha.github.io/genetic.algo.optimizeR/reference/crossover.html below:

Crossover of selected parents from the fitting population — crossover • genetic.algo.optimizeR

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.

crossover(selected_parents, offspring_size)

selected_parents

The list of selected individuals from the population.

offspring_size

The number of offspring that the selected population should have.

The output expected should be a list of offspring for the next generation.

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

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