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.@H_301_2@

这是我的代码如下：@H_301_2@

@H_301_2@

@H_403_8@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.@H_301_2@

更新@H_301_2@

运行的地方@H_301_2@

@H_301_2@

@H_403_8@if __name__ == '__main__':
    algo = GAHello()
    algo.go()

我的输出：@H_301_2@

@H_301_2@

@H_403_8@0 > 1122 Ü<pñsÅá׺Ræ¾
1 > 1015  ÷zËÔ5AÀ©«
2 > 989 "ÆþõZi±Pmê
3 > 1076 ­ ØáíAÀ©«
4 > 1039 #ÆþÕRæ´Ìosß
5 > 946 ×ZÍG¤'ÒÙË
6 > 774 $\àPÉ
7 > 1194 A®Ä§ö
ÝÖ Ð
8 > 479 @r=q^Ü´{J
9 > 778 X'YþH_õÏÆ
10 > 642 z¶$oKÐ{
...
172 > 1330 ê¸EïôÀ«ä£ü
173 > 1085 ÔOÕÛ½e·À×äÒU
174 > 761 OÕÛ½¤¯£+} 
175 > 903 P½?-´ëÎm|4Ô
176 > 736 àPSÈe<1
177 > 1130 ªê/*ñ¤îã¹¾^
178 > 772 OÐS8´°jÓ£
...
990 > 1017 6ó¨QøÇ?¨Úí
991 > 1006 |5ÇÐR·Ü¸í
992 > 968 ×5QÍË?1V í
993 > 747 B ªÄ*¶R·Ü$F
994 > 607  `ªLaøVLº
995 > 744 Ìx7eøi;ÄÝ[
996 > 957 ¹8/ñ^ ¤
997 > 916 Ú'dúý8}û« [
998 > 892 ÛWòeTùv­6ç®
999 > 916 õg8g»}à³À

样本输出应该是：@H_301_2@

@H_301_2@

@H_403_8@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!