import pygad

import numpy

import random

# Global constants for testing

num_generations = 5

num_parents_mating = 4

sol_per_pop = 10

num_genes = 10

random_seed = 42

def fitness_func(ga_instance, solution, solution_idx):

return numpy.sum(solution)

def fitness_func_multi(ga_instance, solution, solution_idx):

return [numpy.sum(solution), numpy.sum(solution**2)]

def run_ga_with_params(parent_selection_type='sss', crossover_type='single_point', mutation_type='random', multi_objective=False):

if multi_objective:

f = fitness_func_multi

else:

f = fitness_func

ga_instance = pygad.GA(num_generations=num_generations,

num_parents_mating=num_parents_mating,

fitness_func=f,

sol_per_pop=sol_per_pop,

num_genes=num_genes,

parent_selection_type=parent_selection_type,

crossover_type=crossover_type,

mutation_type=mutation_type,

random_seed=random_seed,

suppress_warnings=True)

ga_instance.run()

return ga_instance

def test_selection_operators():

operators = ['sss', 'rws', 'sus', 'rank', 'random', 'tournament']

for op in operators:

ga = run_ga_with_params(parent_selection_type=op)

# Verify parents were selected

assert ga.last_generation_parents.shape == (num_parents_mating, num_genes)

print(f"Selection operator '{op}' passed.")

def test_crossover_operators():

operators = ['single_point', 'two_points', 'uniform', 'scattered']

for op in operators:

ga = run_ga_with_params(crossover_type=op)

# Verify population shape

assert ga.population.shape == (sol_per_pop, num_genes)

print(f"Crossover operator '{op}' passed.")

def test_mutation_operators():

operators = ['random', 'swap', 'inversion', 'scramble']

for op in operators:

ga = run_ga_with_params(mutation_type=op)

# Verify population shape

assert ga.population.shape == (sol_per_pop, num_genes)

print(f"Mutation operator '{op}' passed.")

def test_multi_objective_selection():

# NSGA-II is usually used for multi-objective

ga = run_ga_with_params(parent_selection_type='nsga2', multi_objective=True)

assert ga.last_generation_parents.shape == (num_parents_mating, num_genes)

print("Multi-objective selection (nsga2) passed.")

# Tournament NSGA-II

ga = run_ga_with_params(parent_selection_type='tournament_nsga2', multi_objective=True)

assert ga.last_generation_parents.shape == (num_parents_mating, num_genes)

print("Multi-objective selection (tournament_nsga2) passed.")

if __name__ == "__main__":

test_selection_operators()

test_crossover_operators()

test_mutation_operators()

test_multi_objective_selection()

print("\nAll operator tests passed!")