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

from pygad import pygad


def fitness_function(solution):
    """
    This is a fitness function which returns the solution's value as is.
    """
    return solution

# Initializing the PyGAD class with the following parameters:
# 1. sol_per_pop: This is the number of solutions in the population (e.g. number of chromosomes).
# 2. num_generations: This is the number of generations (or iterations).
# 3. num_parents_mating: The number of solutions to be selected as parents in the mating process.
# 4. fitness_func: This is the fitness function which is used to evaluate the solutions in the population.
# 5. sol_dimension: This is the dimension of each solution in the population (e.g. the number of parameters of the problem)
# 6. maximize: Set to True if the problem is to be maximized. Otherwise, set to False.
# 7. mutation_percent_genes: The percentage of genes to mutate in the mutation process.

num_generations = 5
sol_per_pop = 8