pop_size = 500  # 種群數(shù)量 
max_value = 10  # 基因中允許出現(xiàn)的最大值 
chrom_length = 10  # 染色體長度 
pc = 0.6   # 交配概率 
pm = 0.01   # 變異概率 
results = [[]]  # 存儲每一代的最優(yōu)解，N個二元組 
fit_value = []  # 個體適應度 
fit_mean = []  # 平均適應度 
pop = geneEncoding(pop_size, chrom_length) 

def geneEncoding(pop_size, chrom_length): 
 pop = [[]] 
 for i in range(pop_size): 
  temp = [] 
  for j in range(chrom_length): 
   temp.append(random.randint(0, 1)) 
  pop.append(temp) 
 return pop[1:] 

# 0.0 coding:utf-8 0.0 
# 解碼并計算值 
import math 
def decodechrom(pop, chrom_length): 
 temp = [] 
 for i in range(len(pop)): 
  t = 0 
  for j in range(chrom_length): 
   t += pop[i][j] * (math.pow(2, j)) 
  temp.append(t) 
 return temp 
 
 
def calobjValue(pop, chrom_length, max_value): 
 temp1 = [] 
 obj_value = [] 
 temp1 = decodechrom(pop, chrom_length) 
 for i in range(len(temp1)): 
  x = temp1[i] * max_value / (math.pow(2, chrom_length) - 1) 
  obj_value.append(10 * math.sin(5 * x) + 7 * math.cos(4 * x)) 
 return obj_value 

# 0.0 coding:utf-8 0.0 
# 淘汰（去除負值） 
def calfitValue(obj_value): 
 fit_value = [] 
 c_min = 0 
 for i in range(len(obj_value)): 
  if(obj_value[i] + c_min > 0): 
   temp = c_min + obj_value[i] 
  else: 
   temp = 0.0 
  fit_value.append(temp) 
 return fit_value 

# 0.0 coding:utf-8 0.0 
# 選擇 
import random 
def sum(fit_value): 
 total = 0 
 for i in range(len(fit_value)): 
  total += fit_value[i] 
 return total 
def cumsum(fit_value): 
 for i in range(len(fit_value)-2, -1, -1): 
  t = 0 
  j = 0 
  while(j <= i): 
   t += fit_value[j] 
   j += 1 
  fit_value[i] = t 
  fit_value[len(fit_value)-1] = 1 
def selection(pop, fit_value): 
 newfit_value = [] 
 # 適應度總和 
 total_fit = sum(fit_value) 
 for i in range(len(fit_value)): 
  newfit_value.append(fit_value[i] / total_fit) 
 # 計算累計概率 
 cumsum(newfit_value) 
 ms = [] 
 pop_len = len(pop) 
 for i in range(pop_len): 
  ms.append(random.random()) 
 ms.sort() 
 fitin = 0 
 newin = 0 
 newpop = pop 
 # 轉輪盤選擇法 
 while newin < pop_len: 
  if(ms[newin] < newfit_value[fitin]): 
   newpop[newin] = pop[fitin] 
   newin = newin + 1 
  else: 
   fitin = fitin + 1 
 pop = newpop 

# 0.0 coding:utf-8 0.0 
# 交配 
import random 
def crossover(pop, pc): 
 pop_len = len(pop) 
 for i in range(pop_len - 1): 
  if(random.random() < pc): 
   cpoint = random.randint(0,len(pop[0])) 
   temp1 = [] 
   temp2 = [] 
   temp1.extend(pop[i][0:cpoint]) 
   temp1.extend(pop[i+1][cpoint:len(pop[i])]) 
   temp2.extend(pop[i+1][0:cpoint]) 
   temp2.extend(pop[i][cpoint:len(pop[i])]) 
   pop[i] = temp1 
   pop[i+1] = temp2 

# 0.0 coding:utf-8 0.0 
# 基因突變 
import random 
def mutation(pop, pm): 
 px = len(pop) 
 py = len(pop[0]) 
 for i in range(px): 
  if(random.random() < pm): 
   mpoint = random.randint(0, py-1) 
   if(pop[i][mpoint] == 1): 
    pop[i][mpoint] = 0 
   else: 
    pop[i][mpoint] = 1 

# 0.0 coding:utf-8 0.0 
import matplotlib.pyplot as plt 
import math 
from calobjValue import calobjValue 
from calfitValue import calfitValue 
from selection import selection 
from crossover import crossover 
from mutation import mutation 
from best import best 
from geneEncoding import geneEncoding 
print 'y = 10 * math.sin(5 * x) + 7 * math.cos(4 * x)' 
# 計算2進制序列代表的數(shù)值 
def b2d(b, max_value, chrom_length): 
 t = 0 
 for j in range(len(b)): 
  t += b[j] * (math.pow(2, j)) 
 t = t * max_value / (math.pow(2, chrom_length) - 1) 
 return t 
pop_size = 500  # 種群數(shù)量 
max_value = 10  # 基因中允許出現(xiàn)的最大值 
chrom_length = 10  # 染色體長度 
pc = 0.6   # 交配概率 
pm = 0.01   # 變異概率 
results = [[]]  # 存儲每一代的最優(yōu)解，N個二元組 
fit_value = []  # 個體適應度 
fit_mean = []  # 平均適應度 
# pop = [[0, 1, 0, 1, 0, 1, 0, 1, 0, 1] for i in range(pop_size)] 
pop = geneEncoding(pop_size, chrom_length) 
for i in range(pop_size): 
 obj_value = calobjValue(pop, chrom_length, max_value)  # 個體評價 
 fit_value = calfitValue(obj_value)  # 淘汰 
 best_individual, best_fit = best(pop, fit_value)  # 第一個存儲最優(yōu)的解, 第二個存儲最優(yōu)基因 
 results.append([best_fit, b2d(best_individual, max_value, chrom_length)]) 
 selection(pop, fit_value)  # 新種群復制 
 crossover(pop, pc)  # 交配 
 mutation(pop, pm)  # 變異 
results = results[1:] 
results.sort() 
X = [] 
Y = [] 
for i in range(500): 
 X.append(i) 
 t = results[i][0] 
 Y.append(t) 
plt.plot(X, Y) 
plt.show()