Hello World, Genetic Algorithm: https://www.electricmonk.nl/log/2011/09/28/evolutionary-algorithm-evolving-hello-world/

genetic_algorithm.py

# source : https://www.electricmonk.nl/log/2011/09/28/evolutionary-algorithm-evolving-hello-world/
import random
import string

source = "jijvdvnmertnj"
target = "Hello, World!"

def calc_fitness(source, target):
    fitval = 0
    for i in range(0, len(source)):
        fitval += (ord(target[i]) - ord(source[i])) ** 2
    return fitval

GEN_SIZE = 20
genepool = []
for i in range(0, GEN_SIZE):
    dna       = [random.choice(string.printable[:-5]) for j in range(0, len(target))]
    fitness   = calc_fitness(dna, target)
    candidate = {'dna': dna, 'fitness': fitness}
    genepool.append(candidate)

def mutate(parent1, parent2):
    child_dna = parent1['dna'][:]
    # mix both dnas
    start = random.randint(0, len(parent2['dna'])-1)
    stop  = random.randint(0, len(parent2['dna'])-1)
    if start>stop:
        stop, start = start, stop
    child_dna[start:stop] = parent2['dna'][start:stop]
    # mutate one position
    charpos = random.randint(0, len(child_dna)-1)
    child_dna[charpos] = chr(ord(child_dna[charpos])+random.randint(-1,1))
    child_fitness = calc_fitness(child_dna, target)
    return ({'dna': child_dna, 'fitness': child_fitness})

def random_parent(genepool):
    wRndNr = random.random()*random.random()*(GEN_SIZE-1)
    wRndNr = int(wRndNr)
    return (genepool[wRndNr])

while True:
    genepool.sort(key=lambda candidate: candidate['fitness'])
    if genepool[0]['fitness'] == 0:
        print("Target reached")
        print(str(genepool[0]['dna']))
        break
    parent1 = random_parent(genepool)
    parent2 = random_parent(genepool)

    child = mutate(parent1, parent2)
    if child['fitness'] < genepool[-1]['fitness']:
        genepool[-1] = child