我无法使用python中的遗传算法得到正确的答案

我试图用python写一个简单的生成算法,应该给我提供“ Hello World”.它工作正常,但无法通过“最大迭代”常量给出核心答案.它只是在无限循环中工作.

这是我的代码如下：

import random

class GAHello():
    POPULATION_SIZE = 1000
    ELITE_RATE = 0.1
    SURVIVE_RATE = 0.5
    MUTATION_RATE = 0.2
    TARGET = "Hello World!"
    MAX_ITER = 1000

    def InitializePopulation(self):
        tsize: int = len(self.TARGET)
        population = list()

        for i in range(0,self.POPULATION_SIZE):
            str = ''
            for j in range(0,tsize):
                str += chr(int(random.random() * 255))

            citizen: Genome = Genome(str)
            population.append(citizen)
        return population

    def Mutation(self,strng):
        tsize: int = len(self.TARGET)
        ipos: int = int(random.random() * tsize)
        delta: chr = chr(int(random.random() * 255))

        return strng[0: ipos] + delta + strng[ipos + 1:]

    def mate(self,population):
        esize: int = int(self.POPULATION_SIZE * self.ELITE_RATE)
        tsize: int = len(self.TARGET)

        children = self.select_elite(population,esize)

        for i in range(esize,self.POPULATION_SIZE):
            i1: int = int(random.random() * self.POPULATION_SIZE * self.SURVIVE_RATE)
            i2: int = int(random.random() * self.POPULATION_SIZE * self.SURVIVE_RATE)
            spos: int = int(random.random() * tsize)

            strng: str = population[i1][0: spos] + population[i2][spos:]
            if(random.random() < self.MUTATION_RATE):
                strng = self.Mutation(strng)

            child = Genome(strng)
            children.append(child)

        return children

    def go(self):
        popul = self.InitializePopulation()

        for i in range(0,self.MAX_ITER):
            popul.sort()
            print("{} > {}".format(i,str(popul[0])))

            if(popul[0].fitness == 0):
                break
            popul = self.mate(popul)

    def select_elite(self,population,esize):
        children = list()
        for i in range(0,esize):
            children.append(population[i])

        return children



class Genome():
    strng = ""
    fitness = 0

    def __init__(self,strng):
        self.strng = strng
        fitness = 0
        for j in range(0,len(strng)):
            fitness += abs(ord(self.strng[j]) - ord(GAHello.TARGET[j]))

        self.fitness = fitness

    def __lt__(self,other):
        return self.fitness - other.fitness

    def __str__(self):
        return "{} {}".format(self.fitness,self.strng)

    def __getitem__(self,item):
        return self.strng[item]

谢谢你的建议.我真的是菜鸟,并且我只是训练和试验这样的算法和优化的东西来探索人工智能方法.

更新

运行的地方

if __name__ == '__main__':
    algo = GAHello()
    algo.go()

我的输出：

0 > 1122 ü<p?s?á×oR??
1 > 1015  ÷z??5Aà??
2 > 989 "?t?Zi±Pmê
3 > 1076 - ?áíAà??
4 > 1039 #?t?R?′ìos?
5 > 946 ×ZíG¤'òù?
6 > 774 $àPé
7 > 1194 A??§?
Y? D
8 > 479 @r=q^ü′{J
9 > 778 X'YtH_???
10 > 642 z?$oKD{
...
172 > 1330 ê?E??à??￡ü
173 > 1085 ?O???e·à×?òU
174 > 761 O???¤ˉ￡+} 
175 > 903 P??-′??m|4?
176 > 736 àPSèe<1
177 > 1130 aê/*?¤??1?^
178 > 772 ODS8′°jó￡
...
990 > 1017 6ó¨Q???¨úí
991 > 1006 |5?DR·ü??í
992 > 968 ×5Qí??1V í
993 > 747 B?a?*?R·ü$F
994 > 607 ?`aLa?VLo
995 > 744 ìx7e?i;?Y[
996 > 957 18/?^ ¤
997 > 916 ú'dúy8}?? [
998 > 892 ?WòeTùv-6??
999 > 916 ?g8g?}à3à

样本输出应该是：

0 > 419 Un~?z^Kr??p┬
1 > 262 Un~?z^Kr?j??
2 > 262 Un~?z^Kr?j??
…
15 > 46 Afpdm'Ynosa"
16 > 46 Afpdm'Ynosa"
17 > 42 Afpdm'Ynoia"
18 > 27 Jfpmm↓Vopoa"
…
33 > 9 Ielmo▼Wnole"
34 > 8 Ielmo▲Vopld"
35 > 8 Ielmo▲Vopld"
…
50 > 1 Hello World"
51 > 1 Hello World"
52 > 0 Hello World!