Python 3.5 - 遗传算法循环

Question

from pylab import * 
import numpy as np 
import sys 
def initial(): 
    generation = [0,0,1,1,1,1,1,1,1,0,0,0,0,0] 
    generation = generation 
    return generation 
def fitness(flag): 
    global transfer 
    transfer = [] 
    newgeneration1 = pairing() 
    scores = [1,2,3,7,6,5,3] 
    if flag < 1: 
     generation = initial() 
    else: 
     generation = newgeneration1 
    transfer.append(generation) 
    transfer.append(scores) 
    print(transfer) 
    return transfer 
def pairing(): 
    transfer = fitness(i) 
    scores = transfer[1] 
    generation1 = transfer[0] 
    newgeneration = [1,0,1,0,0,0,0,0,1,0,1,1,1,1] 
    return newgeneration 

initial() 
for i in range(3): 
    fitness(i) 
    pairing() 
    if i == 3: 
     scores = fitness(i) 
     print("The following is the final generation: ") 
     print(pairing(i-1)) 
     print("Here are the scores: ") 
     print(scores) 
     sys.exit() 

以上是我在python 3.5中的遗传算法代码的一个简化版本，当我运行这个时我收到一个错误，说：超过最大递归深度，I我试图让它做一次初始函数，然后在适应度和配对之间循环一定次数的迭代，问题是，配对（）创建新一代，并且健身需要采用新一代并确定其适应度，然后它发送到配对，另一个新一代创建..等等。你明白了。预先感谢任何帮助！

Answer 1

这里的问题：

所以，让我们通过你的程序走...

• 首先调用initial()
• 接下来你叫fitness(i)
• 你叫paring的fitness函数内在01240702892845267539里面函数调用fitness
• 你叫paring ....

什么，你在这里得到的是一个无限循环，因为fitness和paring保持通话对方

这里的fitness函数内解决方案：

def pairing(): 
    global transfer # transfer is a global variable, you don't need to call a function to get it 
    #transfer = fitness(i) 
    #scores = transfer[1] # you've already defined these variables, no need to define them again 
    #generation1 = transfer[0] 
    newgeneration = [1,0,1,0,0,0,0,0,1,0,1,1,1,1] 
    return newgeneration 

我已经取出了一些不必要的锂从您的配对功能NES，程序现在工作，因为它应该

下面是输出：

[[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [1, 2, 3, 7, 6, 5, 3]] 
[[1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1], [1, 2, 3, 7, 6, 5, 3]] 
[[1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1], [1, 2, 3, 7, 6, 5, 3]]