class Experiment(object):
POP_SIZE = 500
RANDOM_ACTORS_NUMBER = 50
RANK_PROBABILITY_CONSTANT = 0.2
def __init__(self):
self.world = World(self)
self.population = Population(rank_probability=self.RANK_PROBABILITY_CONSTANT, reverse_sort=False)
self.pop_index = 1
self.current_generation = 1
def start(self):
for _ in xrange(self.POP_SIZE):
self.population.append(self.create_actor())
self.world.start()
@staticmethod
def evaluate_fitness(actor):
if actor.dead:
return 0xFFFFFFF
x = actor.position[0] - float(actor.world.point[0])
y = actor.position[1] - float(actor.world.point[1])
vec_len = float(math.sqrt((x**2) + (y**2)))
color_diff = float(abs(0x00F - actor.body.color))
vertex_handicap = float(len(actor.body.polygon)**3)
return vec_len + color_diff + vertex_handicap
def create_actor(self, genotype=None):
actor = Actor(self.world, self.pop_index, self.evaluate_fitness, genotype=genotype, position=(0, 0))
self.pop_index += 1
return actor
def next_generation(self):
self.world.stop()
self.population.evaluate()
new_pop = Population(rank_probability=self.RANK_PROBABILITY_CONSTANT, reverse_sort=False)
for _ in xrange(self.RANDOM_ACTORS_NUMBER):
new_pop.append(self.create_actor())
for _ in xrange(self.POP_SIZE - self.RANDOM_ACTORS_NUMBER):
new_genotype = Genotype.reproduce(self.population.select_by_rank().genotype,
self.population.select_by_rank().genotype)
new_pop.append(self.create_actor(genotype=new_genotype))
self.population = new_pop
self.current_generation += 1
self.world.start()
def update(self):
for actor in self.population:
actor.update()
def stop(self):
self.world.stop()
self.population.select_best_fitness().brain_graph()
