Haz un ejemplo de la aplicación de la librería PyGAD Python para algoritmos genéticos

from pygad import GA
import numpy as np

# Define the function that the GA will optimise.
def fitness_function(solution):
    x, y = solution
    return (x - 2) ** 2 + (y - 2) ** 2

ga = GA(num_generations=50,
        sol_per_pop=50,
        num_parents_mating=10,
        fitness_func=fitness_function,
        crossover_prob=0.7,
        mutation_prob=0.1,
        lower_bound=[-100, -100],
        upper_bound=[100, 100])

ga.run()

print(ga.best_solution())
print(ga.best_solution_fitness())