Roulette Selection in Genetic Algorithms

Question

Can anyone provide some pseudo code for a roulette selection function? How would I implement this:

Answer 1

Here is the code in python. This code can also handle the negative value of fitness.

from numpy import min, sum, ptp, array 
from numpy.random import uniform 

list_fitness1 = array([-12, -45, 0, 72.1, -32.3])
list_fitness2 = array([0.5, 6.32, 988.2, 1.23])

def get_index_roulette_wheel_selection(list_fitness=None):
    """ It can handle negative also. Make sure your list fitness is 1D-numpy array"""
    scaled_fitness = (list_fitness - min(list_fitness)) / ptp(list_fitness)
    minimized_fitness = 1.0 - scaled_fitness
    total_sum = sum(minimized_fitness)
    r = uniform(low=0, high=total_sum)
    for idx, f in enumerate(minimized_fitness):
        r = r + f
        if r > total_sum:
            return idx

get_index_roulette_wheel_selection(list_fitness1)
get_index_roulette_wheel_selection(list_fitness2)

1. Make sure your fitness list is 1D-numpy array
2. Scaled the fitness list to the range [0, 1]
3. Transform maximum problem to minimum problem by 1.0 - scaled_fitness_list
4. Random a number between 0 and sum(minimizzed_fitness_list)
5. Keep adding element in minimized fitness list until we get the value greater than the total sum
6. You can see if the fitness is small --> it has bigger value in minimized_fitness --> It has a bigger chance to add and make the value greater than the total sum.