def select(k, kp, evaluations):
n = len(evaluations)
new_population = []
for _ in range(n):
selecteds = []
for _ in range(k):
selecteds.append(evaluations[lottery.range((0, n - 1))])
if kp >= lottery.range((0, 10000)) / 10000:
new_population.append(__get_best(selecteds))
else:
new_population.append(__get_worse(selecteds))
return new_population
def select(evaluations):
relative_evaluations = relative_fitness.get(evaluations)
n = len(evaluations)
new_population = []
for _ in range(int(n / 2)):
index = lottery.range((0, n - 1))
while (not lottery.roll(int(relative_evaluations[index][1]))):
index = lottery.range((0, n - 1))
new_population.append(relative_evaluations[index][0])
index_2 = lottery.range((0, n - 1))
while (index_2 != index
and not lottery.roll(int(relative_evaluations[index_2][1]))):
index_2 = lottery.range((0, n - 1))
new_population.append(relative_evaluations[index_2][0])
return new_population
def select(d, evaluations): n = len(evaluations) new_population = [] for _ in range(n): index = lottery.range((0,n-1)) neighbourhood = [] new_population.append(evaluations[index][0])
def select(evaluations):
n = len(evaluations)
evaluations.sort(key=lambda tup: tup[1])
uniform_fitness = __uniform_fitness(evaluations, n)
new_population = []
while len(new_population) < n:
index = lottery.range((0, n - 1))
if lottery.roll(uniform_fitness[index][1]):
new_population.append(uniform_fitness[index][0])
return new_population
def __single_point(parent_1, parent_2, prob):
if not lottery.roll(prob):
return (parent_1, parent_2)
point = lottery.range((0, len(parent_1)))
children_1 = []
children_2 = []
for i in range(point):
children_1.append(parent_1[i])
children_2.append(parent_2[i])
for i in range(point, len(parent_1)):
children_1.append(parent_2[i])
children_2.append(parent_1[i])
return (children_1, children_2)
def __float_subject(size, bounds): subject = [] realBounds = (bounds[0]*100, bounds[1]*100) for _ in range(size): subject.append(lottery.range(realBounds)/100) return subject
def __integer_subject(size, bounds): subject = [] for _ in range(size): subject.append(lottery.range(bounds)) return subject
def __binary_subject(size, bounds):
subject = []
for _ in range(size):
subject.append(lottery.range((0,1)))
return subject
