robots = []

num_stay_put = 0

def __init__(self, id, robots=None):

self.id = id

if robots is None:

for i in range(POPULATION_SIZE):

self.robots.append(Robby.get_random_robby())

else:

self.robots = robots

@staticmethod

def get_roulette_wheel_selection(size):

return 200 - (int((2 * random.randint(1, size + 1)) ** 0.5))

def get_score(self):

r_val = 0.0

for i in range(len(self.robots)):

m = MultipleSessions(self.robots[i])

r_val = r_val + m.run()

return r_val / len(self.robots)

def apply_evolution(self):

tuples = []

total_score = 0.0

MultipleSessions.refresh_grids()

for i in range(len(self.robots)):

m = MultipleSessions(self.robots[i])

score = m.run()

total_score = total_score + score

tuples.append((self.robots[i], score))

tuples.sort(key=lambda x: x[1], reverse=True)

normalized_score = total_score / len(self.robots)

best_score = tuples[0][1]

child_robots = []

roulette_size = (POPULATION_SIZE * (POPULATION_SIZE + 1) / 2)

for i in range(POPULATION_SIZE / 2):

parent_1 = tuples[self.get_roulette_wheel_selection(roulette_size)][0]

parent_2 = tuples[self.get_roulette_wheel_selection(roulette_size)][0]

child_1, child_2 = parent_1.give_birth(parent_2)

child_robots.append(child_1)

child_robots.append(child_2)

self.num_stay_put = child_1.gene.count(5) + child_2.gene.count(5)