Yes, I wrote a code to win an internet argument. Look how bored I was.

gistfile1.txt

import numpy as np
from tqdm import tqdm

class GenAlgWithoutPopulationRestriction():
    
    def __init__(
        self, N_init=10, target_string="ESCHO SPORIT SO MNOY BUDESH LOLKA SASAII",
        temperature=1, pm=0.5, pmating=0.6
    ):
        self.alphabet = [a for a in "ABCDEFGHIJKLMNOPQRSTUVWXYZ "]
        self.ts = [a for a in target_string]
        self.population = [self.generate_random() for a in np.arange(N_init)]
        self.p_mutation = pm
        self.temperature = temperature
        self.p_mating = pmating
        
    def generate_random(self):
        return(np.random.choice(self.alphabet, len(self.ts)))
    
    def fitness(self, s):
        return(np.sum([1 if a == b else 0 for a,b in zip(s, self.ts)]))
    
    def crossover(self, s1, s2):
        mid = int(len(s1)/2)
        s3 = list(s1[0:mid])+list(s2[mid:])
        s4 = list(s2[0:mid])+list(s1[mid:])
        return([s3, s4])
    
    def mutate(self, s1):
        s2 = s1[:]
        s2[np.random.choice(np.arange(len(s1)))] = np.random.choice(self.alphabet)
        return(s2)
    
    def softmax(self, d):
        e = np.exp(np.array(d)/self.temperature)
        return(e/np.sum(e))
    
    def mate(self, ffs):
        pdist = self.softmax(ffs)
        inds = np.random.choice(np.arange(len(self.population)), 2, p=pdist)
        return(self.population[inds[0]], self.population[inds[1]])
    
    def mating_selection(self, n_epochs=100):
        for a in tqdm(list(range(n_epochs))):
            if np.random.choice([True, False], p=[self.p_mutation, 1-self.p_mutation]):
                chosen = np.random.choice(np.arange(len(self.population)))
                self.population[chosen] = self.mutate(self.population[chosen])
            if np.random.choice([True, False], p=[self.p_mating, 1-self.p_mating]):
                ffs = [self.fitness(a) for a in self.population]
                m1,m2 = self.mate(ffs)
                self.population.extend(self.crossover(m1, m2))

    def plot_top(self, n):
        ordered = list(reversed(list(sorted(self.population, key=self.fitness))))
        return(["".join(a) for a in ordered[0:n]])
                

g = GenAlgWithoutPopulationRestriction()
g.mating_selection(10000)