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import math
import random
import time,os
from matplotlib.pyplot import *
import numpy as np
class Evolution():
def __init__(self, population, mutation_rate, add, retain_rate, data):
self.num = population
self.mutation_rate = mutation_rate
self.add = add
self.retain_rate = retain_rate
self.best = [[1,2],float("inf")]
self.data = data
self.mutationnum = 0
self.insert = range(0, len(self.data))
self.AllMaxy = []
self.d = 0
self.population = self.Gene_population()
matplotlib.use('Agg')
def Gene_population(self):
"""
获取初始种群（一个含有 self.num 个长度为 self.chromosome_size 的染色体的列表）
"""
x = []
newx = range(0, len(self.data))
while len(x) < self.num:
if newx not in x:
x += [newx[:]]
random.shuffle(newx)
return x[:]
def Evolve(self):
"""
进化
对当前一代种群依次进行选择、交叉并生成新一代种群，然后对新一代种群进行变异
"""
self.Selection()
result = '目前第 ' + str(self.d) + ' 代, 此代最优为 ' + str(1 / self.Fitness(self.population[0])) + ' , 变异次数为 ' + str(self.mutationnum)
self.Crossover()
self.Force_Insert()
self.Mutation()
self.d += 1
return result
def Fitness(self, xi):
"""
计算适应度，将染色体解码为定义域之间的数字，代入函数计算
因为是求最大值，所以数值越大，适应度越高
"""
start = 0
distance = 0.0
for end in range(1, len(xi)):
distance += ((self.data[xi[start]][1][0] * 1.0 - self.data[xi[end]][1][0]) ** 2 + (self.data[xi[start]][1][1] * 1.0 - self.data[xi[end]][1][1]) ** 2) ** 0.5
start = end
return 1 / distance
def Selection(self):
"""
选择
先对适应度从大到小排序，选出存活的染色体
再进行随机选择，选出适应度虽然小，但是幸存下来的个体
"""
xy = sorted([(i, j) for i, j in [(self.Fitness(xi), xi) for xi in self.population]], reverse = True)
x = [x1 for y1, x1 in xy]
y = [y1 for y1, x1 in xy]
eMaxy = 1 / y[0]
self.AllMaxy += [eMaxy]
self.population = x[:int(self.num * self.retain_rate)]
if eMaxy < self.best[1]:
self.PlotAndSave(x[0], y[0], 1)
os.system('cls')
print x[0], '\n', '最佳第', self.d, '代', '最优路径长度为', eMaxy
self.best = [x[0][:],eMaxy]
def Crossover(self):
"""
染色体的交叉、繁殖，生成新一代的种群
新出生的孩子，最终会被加入存活下来的父母之中，形成新一代的种群。
"""
length = len(self.population)
for i in range(length):
father= random.choice(self.population)
mother = random.choice(self.population)
r1 = np.random.random_integers(0, len(father) - 1)
r2 = np.random.random_integers(1, len(mother) - r1)
DNA1 = father[r1:r1 + r2]
DNA2 = []
for i in range(len(mother)):
if mother[i] not in DNA1:
DNA2 += [mother[i]]
child = []
for i in range(r1):
child += [DNA2[i]]
child += DNA1 + DNA2[r1:]
self.population += [child[:]]
def Force_Insert(self):
while len(self.population) < self.num:
self.population += [self.insert[:]]
random.shuffle(self.insert)
def Mutation(self):
"""
变异
对种群中的所有个体，随机改变某个个体中的某个基因
"""
each_mutation_rate = self.mutation_rate
for i in range(len(self.population)):
if random.uniform(0,1) < each_mutation_rate:
r = np.random.randint(0, len(self.population[i]))
self.population[i][r], self.population[i][len(self.population[i]) - r - 1] = self.population[i][len(self.population[i]) - r - 1], self.population[i][r]
self.mutationnum += 1
each_mutation_rate += self.add
def PlotAndSave(self, x, y, f):
plot(range(1, len(self.AllMaxy) + 1), self.AllMaxy, 'b')
title(str(x) + '\n\n' + str(1 / y), fontsize = 7)
if f:
savefig('Max.jpg')
else:
savefig('%d_Max.jpg' %self.d)
close('all')
xr = []
yr = []
for i in x:
xr += [self.data[i][1][0]]
yr += [self.data[i][1][1]]
xr = np.array(xr + [xr[0]])
yr = np.array(yr + [yr[0]])
x0 = xr[:2]
y0 = yr[:2]
xr = xr[1:]
yr = yr[1:]
quiver(x0[:-1], y0[:-1], x0[1:] - x0[:-1], y0[1:] - y0[:-1], scale_units = 'xy', angles = 'xy', scale = 1, color='r')
quiver(xr[:-1], yr[:-1], xr[1:] - xr[:-1], yr[1:] - yr[:-1], scale_units = 'xy', angles = 'xy', scale = 1)
if f:
savefig('Max_Road.jpg')
else:
savefig('%d_Road.jpg' %self.d)
close('all')
time.clock()
population = 1000
mutation_rate = 0.001
add = 0.05
retain_rate = 0.5
data = [["*", [37, 52]], ["*", [49, 49]], ... ,["*", [30,40]]]
gene = Evolution(population, mutation_rate, add, retain_rate, data)
while 1:
t = time.clock()
minute, second = divmod(t, 60)
hour, minute = divmod(minute, 60)
try:
print '\r', gene.Evolve(), " 花费时间 %02d:%02d:%02d" %(hour, minute, second),
except:
gene.PlotAndSave(gene.population[:][0], gene.Fitness(gene.population[0]), 0)
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