0
演變函數與變種函數一樣存在問題。Python遺傳算法
from random import randint, random
from operator import add
from functools import reduce
def individual(length, min, max):
'Create a member of the population.'
return [randint(min,max) for x in range(length)]
def population(count, length, min, max):
'Create a number of individuals (i.e. a population).'
return [ individual(length, min, max) for x in range(count) ]
def fitness(individual, target):
'Determine the fitness of an individual. Lower is better.'
sum = reduce(add, individual, 0)
return abs(target-sum)
def grade(pop, target):
'Find average fitness for a population.'
summed = reduce(add, (fitness(x, target) for x in pop), 0)
return summed/(len(pop) * 1.0)
chance_to_mutate = 0.01
for i in p:
if chance_to_mutate > random():
place_to_modify = randint(0,len(i))
i[place_to_modify] = randint(min(i), max(i))
def evolve(pop, target, retain=0.2, random_select=0.05, mutate=0.01):
graded = [(fitness(x, target), x) for x in pop]
graded = [x[1] for x in sorted(graded)]
retain_length = int(len(graded)*retain)
parents = graded[:retain_length]
# randomly add other individuals to promote genetic diversity
for individual in graded[retain_length:]:
if random_select > random():
parents.append(individual)
# mutate some individuals
for individual in parents:
if mutate > random():
pos_to_mutate = randint(0, len(individual)-1)
# this mutation is not ideal, because it
# restricts the range of possible values,
# but the function is unaware of the min/max
# values used to create the individuals,
individual[pos_to_mutate] = randint(
min(individual), max(individual))
# crossover parents to create children
parents_length = len(parents)
desired_length = len(pop) - parents_length
children = []
while len(children) < desired_length:
male = randint(0, parents_length-1)
female = randint(0, parents_length-1)
if male != female:
male = parents[male]
female = parents[female]
half = len(male)/2
child = male[:half] + female[half:]
children.append(child)
parents.extend(children)
return parents
target = 371
p_count = 100
i_length = 5
i_min = 0
i_max = 100
p = population(p_count, i_length, i_min, i_max)
fitness_history = [grade(p, target),]
for i in range(100):
p = evolve(p, target)
fitness_history.append(grade(p, target))
for datum in fitness_history:
print(datum)
我下面這個網站http://lethain.com/genetic-algorithms-cool-name-damn-simple/。它是爲Python 2.6編寫的,因此它不適用於3.我已經對它進行了更新,但無法使其工作。
你被關閉,投票;請參閱http://stackoverflow.com/help/how-to-ask並在您的問題中添加足夠的細節,以便人們可以提供幫助,例如對此無能爲力 - 它應該做什麼,與實際做的相比,以及我們如何測試它。 – TessellatingHeckler
沒有'mutate'功能。 –
如果下次你輸入'[ask]'而不是'stackoverflow.com/help/how-to-ask',你可能希望自己保存一些輸入@TessellatingHeckler。 – boardrider