In Roulette Wheel Selection, this is the best way to deal with negative fitness values

roulette_wheel_selection.py

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.