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

import pygad as pg
import numpy as np

# Defining the fitness function. This function receives a list of bit-strings (i.e. the chromosome).
def fitness_func(chromosome):
    # The chromosome is a numpy array of 32-bits. Therefore, we can compute the floating-point number as follows.
    chromosome_sum = np.sum(chromosome)
    # We return the fitness value (the objective value) of the chromosome.
    return chromosome_sum

# The solver performs the minimization of the fitness function. Therefore, the lower the objective value
# the better the solution.
# The user can specify the `number_of_bits` which is the number of bits per chromosome.
solver = pg.genetic_algorithm(population_size=50, number_of_generations=50, fitness_func=fitness_func,
                              number_of_bits=32)

# Solving the problem (running the optimization process).
solver.run()

# Getting the best solution after running the optimization process.
best_solution