def dev_evolve(self):
"""
Should evolve the population by a given number of generations
"""
pop0 = []
for i in range(10):
pop0.append(2. * np.random.rand(5))
ga = Evolver(pop0, eval_one_max, crossover_probability=0.8,
mutation_probability=0.5, degeneration=True)
ga.evolve(ngen=100, verbose=2)
def test__evolve(self):
"""
Should evolve the population by one generation
"""
pop0 = []
for i in range(10):
pop0.append(np.random.rand(5))
ga = Evolver(pop0, eval_one_max, crossover_probability=0.,
mutation_probability=0.)
fit0 = ga.fitness.mean()
ga._evolve()
self.assertEqual(len(ga.generations), 2)
fit1 = ga.fitness.mean()
self.assertGreaterEqual(fit1, fit0)
def test_evaluate(self):
"""
Should register a working evaluate function
"""
pop0 = []
for i in range(100):
pop0.append(np.round(np.random.rand(5)))
ga = Evolver(pop0, eval_one_max)
self.assertTrue(hasattr(ga, 'generations'))
self.assertTrue(isinstance(ga.generations[0], Population))
self.assertTrue(ga.generations[0].individuals is ga.individuals)
self.assertTrue(ga.generations[0].CROSSOVER_PROBABILITY\
is ga.CROSSOVER_PROBABILITY)
self.assertTrue(ga.generations[0].MUTATION_PROBABILITY\
is ga.MUTATION_PROBABILITY)
# should register the passed function 'evaluate' as a helper
self.assertTrue(hasattr(ga, '_evaluate'))
# should have a main evaluate function that updates fitness
self.assertTrue(hasattr(ga, 'evaluate'))
# calling evaluate should update fitness
ga.evaluate()
self.assertTrue(ga.generations[0].fitness is ga.fitness)
# evaluate should also take fixed parameters
# here, individuals should always be the 1st argument
pop0 = np.ones((100, 5))
ga = Evolver(pop0, eval_args_kwargs)
self.assertEqual(ga.fitness.max(), 5)
ga = Evolver(pop0, eval_args_kwargs, 2)
self.assertEqual(ga.fitness.max(), 10)
ga = Evolver(pop0, eval_args_kwargs, 2, 3)
self.assertEqual(ga.fitness.max(), 30)
ga = Evolver(pop0, eval_args_kwargs, 2, foo=10)
self.assertEqual(ga.fitness.max(), 100)
