A "Hello World" genetic algorithm I've made based on the C# code shown by Italo Jose de Oliveira at his TDC São Paulo 2017 presentation

genetic.py

#!/usr/bin/python3
"""
Genetic algorithm example that finds the best string match using the
hamming distance to define an aptitude function.
"""
from random import choice, seed, randrange
from functools import partial
import string

def crossover(a, b):
    idx = randrange(len(a))
    return a[:idx] + b[idx:]

def aptitude(goal, result):
    """Aptitude = String length - Hamming distance"""
    return sum(a == b for a, b in zip(goal, result))

def generate_single(chars, length):
    return "".join(choice(chars) for unused in range(length))

def solve_genetic(goal, chars, psize, keep, select, cross, epochs):
    apt_goal = partial(aptitude, goal)
    gen_rand = partial(generate_single, chars, len(goal))
    population = [gen_rand() for unused in range(psize)]
    for epoch in range(1, epochs + 1):
        selected = sorted(population, key=apt_goal, reverse=True)[:select]
        first = selected[0]
        print("Generation {}: Aptitude = {} ({})"
              .format(epoch, apt_goal(first), first))
        population = [crossover(choice(selected), choice(selected))
                      for unused in range(cross)]
        population.extend(gen_rand() for unused in range(psize - cross - keep))
        population.extend(selected[:keep])

if __name__ == "__main__":
    seed(123)
    solve_genetic(goal="Hello world",
                  chars=string.ascii_letters + " ",
                  psize=100, keep=1, select=50, cross=50, epochs=150)