def test_population_generate(self, simple_evaluation_function):
def init_func():
return 1
pop = Population.generate(init_function=init_func,
eval_function=simple_evaluation_function,
size=200)
assert len(pop) == 200
assert pop.intended_size == 200
assert pop.individuals[0].chromosome == 1
def run_travelling_salesman(population_size: int = 100,
n_iterations: int = 10,
random_seed: int = 0,
n_destinations: int = 50,
concurrent_workers: Optional[int] = None,
n_groups: int = 4,
silent: bool = False):
seed(random_seed)
# Generate some destinations
destinations = [(random(), random()) for _ in range(n_destinations)]
# Given a list of destination indexes, this is our cost function
def evaluate(ordered_destinations: List[int]) -> float:
total = 0
for x, y in zip(ordered_destinations, ordered_destinations[1:]):
coordinates_x = destinations[x]
coordinates_y = destinations[y]
total += sqrt((coordinates_x[0] - coordinates_y[1])**2 +
(coordinates_x[1] - coordinates_y[1])**2)
return total
# This generates a random solution
def generate_solution() -> List[int]:
indexes = list(range(n_destinations))
shuffle(indexes)
return indexes
def print_function(population: Population):
if population.generation % 5000 == 0 and not silent:
print(
f'{population.generation}: {population.documented_best.fitness:1.2f} / '
f'{population.current_best.fitness:1.2f}')
pop = Population.generate(generate_solution,
eval_function=evaluate,
maximize=False,
size=population_size * n_groups,
concurrent_workers=concurrent_workers)
island_evo = (Evolution().survive(fraction=0.5).breed(
parent_picker=pick_random,
combiner=cycle_crossover).mutate(swap_elements, elitist=True))
evo = (Evolution().evaluate(lazy=True).callback(print_function).repeat(
evolution=island_evo,
n=100,
grouping_function=group_stratified,
n_groups=n_groups))
result = pop.evolve(evolution=evo, n=n_iterations)
if not silent:
print(f'Shortest route: {result.documented_best.chromosome}')
print(f'Route length: {result.documented_best.fitness}')
def func_to_optimise(x):
return x * 2
def pick_random_parents(pop):
return random.choice(pop)
random.seed(42)
pop1 = Population(chromosomes=[create_candidate() for _ in range(5)],
eval_function=func_to_optimise,
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
