我无法在 python 中用遗传算法得到正确答案

Question

我正在尝试用 python 编写一个简单的生成算法，它应该给我一个答案 "Hello World"。它工作正常，但它不能用 "max iteration" 常量给我正确的答案。它只是在无限循环中工作。

下面是我的代码：

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]

感谢您的建议。我真的是菜鸟之类的东西，我只是训练和试验这样的算法和优化东西来探索人工智能方法。

更新

它运行的地方

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

我的输出：

0 > 1122 Ü<pñsÅá×ºRæ¾
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·ÜÂ¸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：

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!

Answer 1

我认为您的列表排序是您的主要问题。

popul.sort()

尝试

popul.sort(key=lambda x: x.fitness)

这将根据他们的健康水平对他们进行排序，我相信这正是您的意图。

我还将所有 int(random.random()*255) 更改为 random.randint(30, 125) 以仅获取有效字符，因为我遇到了问题运行。

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