def test_survive_n_works(self, simple_chromosomes,
simple_evaluation_function):
pop1 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop2 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop3 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
assert len(pop1) == len(simple_chromosomes)
assert len(pop2.survive(n=50)) == 50
assert len(pop3.survive(n=75, luck=True)) == 75
def test_survive_n_and_p_works(self, simple_evaluation_function):
chromosomes = list(range(200))
pop1 = Population(chromosomes=chromosomes,
eval_function=simple_evaluation_function)
pop2 = Population(chromosomes=chromosomes,
eval_function=simple_evaluation_function)
pop3 = Population(chromosomes=chromosomes,
eval_function=simple_evaluation_function)
assert len(pop1.survive(fraction=0.5, n=200)) == 100
assert len(pop2.survive(fraction=0.9, n=10)) == 10
assert len(pop3.survive(fraction=0.5, n=190, luck=True)) == 100
def test_survive_p_works(self, simple_chromosomes,
simple_evaluation_function):
pop1 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop2 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop3 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
assert len(pop1) == len(simple_chromosomes)
assert len(pop2.survive(fraction=0.5)) == len(simple_chromosomes) * 0.5
assert len(pop3.survive(fraction=0.1,
luck=True)) == len(simple_chromosomes) * 0.1
def test_breed_amount_works(self, simple_chromosomes,
simple_evaluation_function):
pop1 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop1.survive(n=50).breed(
parent_picker=lambda population: choices(population, k=2),
combiner=lambda mom, dad: (mom + dad) / 2)
assert len(pop1) == len(simple_chromosomes)
pop2 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop2.survive(n=50).breed(
parent_picker=lambda population: choices(population, k=2),
combiner=lambda mom, dad: (mom + dad) / 2,
population_size=400)
assert len(pop2) == 400
assert pop2.intended_size == 400
def test_breed_works_with_kwargs(self, simple_chromosomes,
simple_evaluation_function):
pop1 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop1.survive(n=50).breed(parent_picker=pick_random,
combiner=lambda mom, dad: (mom + dad) / 2,
n_parents=2)
assert len(pop1) == len(simple_chromosomes)
pop2 = Population(chromosomes=simple_chromosomes,
eval_function=simple_evaluation_function)
pop2.survive(n=50).breed(
parent_picker=pick_random,
combiner=lambda *parents: sum(parents) / len(parents),
population_size=400,
n_parents=3)
assert len(pop2) == 400
assert pop2.intended_size == 400
np.random.seed(1234)
def f(x):
return 8 * x - 2 * x**2
def random_guy():
return np.random.randint(low=-1_000_000, high=1_000_000)
initGenerationSize = 100
firstGen = [random_guy() for _ in range(initGenerationSize)]
pop = Population(chromosomes=firstGen, eval_function=f, maximize=True)
survivors = pop.survive(fraction=0.1)
def pickParents(population):
mom, dad = np.random.choice(population, size=2)
return mom, dad
def makeChild(mom, dad):
return (mom + dad) / 2
def randomNoise(individual, sigma):
noise = np.random.normal(loc=0, scale=10) * sigma
return individual + noise
maximize=True)
pop2 = Population.generate(init_function=create_candidate,
eval_function=func_to_optimise,
size=5,
maximize=False)
print("[i for i in pop1]:")
print([i for i in pop1])
print("[i.chromosome for i in pop1]:")
print([i.chromosome for i in pop1])
print("[i.fitness for i in pop1]:")
print([i.fitness for i in pop1])
print("[i.fitness for i in pop1.evaluate()]:")
def produce_clone(parent):
return parent
def add_noise(x):
return 0.1 * (random.random() - 0.5) + x
print("[i.fitness for i in pop1.survive(n=3)]:")
print([i.fitness for i in pop1.survive(n=3)])
print("[i.fitness for i in pop1.survive(n=3).mutate(add_noise)]:")
print([i.fitness for i in pop1.survive(n=3).mutate(add_noise)])
print("[i.fitness for i in pop1.survive(n=3).mutate(add_noise).evaluate()]:")
print([i.fitness for i in pop1.survive(n=3).mutate(add_noise).evaluate()])
def add_noise(chromosome, sigma):
"""
This is a function that will add some noise to the chromosome.
"""
new_x = chromosome[0] + (r() - 0.5) * sigma
new_y = chromosome[1] + (r() - 0.5) * sigma
return new_x, new_y
# We start by defining a population with candidates.
pop = Population(chromosomes=[random_start() for _ in range(200)],
eval_function=func_to_optimise, maximize=True)
# We do a single step here and out comes a new population
pop.survive(fraction=0.5)
# We do two steps here and out comes a new population
(pop
.survive(fraction=0.5)
.breed(parent_picker=pick_random_parents, combiner=make_child))
# We do a three steps here and out comes a new population
(pop
.survive(fraction=0.5)
.breed(parent_picker=pick_random_parents, combiner=make_child)
.mutate(mutate_function=add_noise, sigma=1))
# This is inelegant but it works.
for i in range(5):
pop = (pop
