def test_aeo_production():
new_aeo = aeo.AEO()
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
a = new_aeo._production(search_space.agents[0], search_space.best_agent, 1, 10)
assert type(a).__name__ == "Agent"
def test_aeo_update():
def square(x):
return np.sum(x**2)
new_aeo = aeo.AEO()
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_aeo.update(search_space, square, 1, 10)
def test_aeo_update_decomposition():
def square(x):
return np.sum(x**2)
new_aeo = aeo.AEO()
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_aeo._update_decomposition(search_space.agents, search_space.best_agent, square)
def test_aeo_carnivore_consumption():
new_aeo = aeo.AEO()
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
a = new_aeo._carnivore_consumption(
search_space.agents[0], search_space.agents[1], 0.5
)
assert type(a).__name__ == "Agent"
def test_aeo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_aeo = aeo.AEO()
search_space = search.SearchSpace(n_agents=10, n_iterations=30,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_aeo.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm aeo failed to converge.'
def test_aeo_build():
new_aeo = aeo.AEO()
assert new_aeo.built == True