这是一种遗传算法吗?

Question

我正在尝试制作一种遗传算法，以找到控制台输入中给出的单词。但我不知道我是否成功地做了一个完整的遗传算法。 这是代码：

main.py：

from population import Population
target = input()
maxPop = 10
mutation = 100
print("nnn")
pop = Population(target, maxPop, mutation)

population.py：

import random
from ADN import genetic
class Population:
def __init__(self, target, maxPop, mut):
adn = genetic()
self.popul = []
i = 0
while i < maxPop:
self.popul.append(adn.genFirst(len(target)))
print(self.popul[i])
i+=1
#oldPop = self.popul
#adn.fitness(oldPop, target)
#"""
while target not in self.popul:
oldPop = self.popul
self.popul = adn.fitness(oldPop, target)
if target in self.popul:
return
#"""

ADN.py：

import random
class genetic:
def genFirst(self, length):
bestGenes = ""
self.letters = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890[],. "
word = ""
i = 0
while i < length:
word += random.choice(self.letters)
i+=1
return word
def fitness(self, oldPop, target):
newPop = []
j = 0
for word in oldPop:
newW = ""
for letter in word:
if(letter not in target):
letter = random.choice(self.letters)
else:
if(target.index(letter) != word.index(letter)):
letter = random.choice(self.letters)
newW += letter
newPop.append(newW)
print(newPop)
return newPop

如果它不是一个完整的遗传算法，缺少什么？

Answer 1

不，这不是遗传算法。它甚至不是一种进化算法。它错过了适应度函数，该函数应该计算计算的每个成员的好坏。之后，你应该决定你想要制作哪个代码：遗传的还是进化的。作为初学者，您应该尝试进化算法，它更容易，并且不包含交叉功能(这对初学者来说很难(。 试试这个：

import random
genes = "abcdefghijklmnopqrsttuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ+-[]()1234567890;<>?/ "
target = input()
def genPar(length):
parent = []
for i in range(length):
parent.append(random.choice(genes))
return "".join(parent)
def fitness(parent):
total = 0
for i in range(len(parent)):
if(parent[i] == target[i]):
total += 1
return total
def mutate(parent):
index  = random.choice(range(len(parent)))
child = []
for i in range(len(parent)):
if(i == index):
letter = random.choice(genes)
else:
letter = parent[i]
child.append(letter)
return "".join(child)
parent = genPar(len(target))
bestPar = parent
bestFitness = fitness(parent)
print(parent)
generations = 1
while True:
child = mutate(bestPar)
childFit = fitness(child)
if(childFit > bestFitness):
bestFitness = childFit
bestPar = child
print(child)
generations += 1
if(child == target):
break
print("nGenerations: " + str(generations))

Answer 2

直到你看到初始化 -> 适应度 -> 遗传算子(突变、交叉( -> 适应度 -> 替代周期 你不能说它是 遗传/进化算法 :)...

Answer 3

对于基本的遗传算法，需要使用一些算子选择、适应度、突变、交叉。 您可以根据自己的问题使用不同类型的选择、交叉和突变。 交叉和突变的简单例子。

def single_point_crossover(parent1,parent2):
crossover_point = random.randint(1,9)
#print("crossover point", crossover_point)
child_1 = np.hstack((parent1[0:crossover_point], parent2[crossover_point:]))
child_2 = np.hstack((parent2[:crossover_point],parent1[crossover_point:]))
return child_1,child_2 

def mutation(parent1,parent2):
n = len(parent1)
pos_1 = random.randint(0,n-1)
pos_2 = random.randint(0,n-1)
#print(pos_1, pos_2)
def swap(sol, posA, posB):
result = sol.copy()
elA = sol[posA]
elB = sol[posB]
result[posA] = elB
result[posB] = elA
return result
child1 = swap(parent1, pos_1, pos_2)
child2 = swap(parent2, pos_1, pos_2)
return child1,child2