The function described below selects the 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
- population
The list of individuals of the population
- fitness
The list of individuals(value) obtained from the function of
genetic.algo.optimizeRnamely `evaluate_fitness`.- num_parents
The number of selected individuals that fit the best with the predefined aim.
Value
The output expected should be a list of selected individuals that fit the best with the predefined condition(aim/objective).
Examples
# example of usage
library(genetic.algo.optimizeR)
population <- c(1, 3, 0)
# Evaluate fitness
fitness <- genetic.algo.optimizeR::evaluate_fitness(population)
print("Evaluation:")
#> [1] "Evaluation:"
print(fitness)
#> [1] 1 1 4
# Selection
selected_parents <- genetic.algo.optimizeR::selection(population, fitness, num_parents = 2)
print("Selection:")
#> [1] "Selection:"
print(selected_parents)
#> [1] 1 3