本系列文章的所有源代码都将会开源，需要源代码的小伙伴可以去我的 Github fork！

上一篇文章我们动手实验了用遗传算法求解函数在给定区间的最大值。本篇文章再来看一个实验：用遗传算法破解密码。 
在这个问题中，我们的个体就是一串字符串了，其目的就是找到一个与密码完全相同的字符串。基本步骤与前一篇文章基本类似，不过在本问题中，我们用字符的ASCII值来表示个体(字符串)的DNA。其它的就不多说了，还是看详细代码吧：

import numpy as npclass GeneticAlgorithm(object):"""遗传算法.Parameters:-----------cross_rate: float交配的可能性大小.mutate_rate: float基因突变的可能性大小. n_population: int种群的大小.n_iterations: int迭代次数.password: str欲破解的密码."""def __init__(self, cross_rate, mutation_rate, n_population, n_iterations, password):self.cross_rate = cross_rateself.mutate_rate = mutation_rateself.n_population = n_populationself.n_iterations = n_iterationsself.password = password                                            # 要破解的密码self.password_size = len(self.password)                             # 要破解密码的长度self.password_ascii = np.fromstring(self.password, dtype=np.uint8)  # 将password转换成ASCIIself.ascii_bounder = [32, 126+1]# 初始化一个种群def init_population(self):population = np.random.randint(low=self.ascii_bounder[0], high=self.ascii_bounder[1], size=(self.n_population, self.password_size)).astype(np.int8)return population# 将个体的DNA转换成ASCIIdef translateDNA(self, DNA):                 # convert to readable stringreturn DNA.tostring().decode('ascii')# 计算种群中每个个体的适应度，适应度越高，说明该个体的基因越好def fitness(self, population):match_num = (population == self.password_ascii).sum(axis=1)return match_num# 对种群按照其适应度进行采样，这样适应度高的个体就会以更高的概率被选择def select(self, population):fitness = self.fitness(population) + 1e-4     # add a small amount to avoid all zero fitnessidx = np.random.choice(np.arange(self.n_population), size=self.n_population, replace=True, p=fitness/fitness.sum())return population[idx]# 进行交配def create_child(self, parent, pop):if np.random.rand() < self.cross_rate:index = np.random.randint(0, self.n_population, size=1)                        # select another individual from popcross_points = np.random.randint(0, 2, self.password_size).astype(np.bool)   # choose crossover pointsparent[cross_points] = pop[index, cross_points]                            # mating and produce one child#child = parentreturn parent# 基因突变def mutate_child(self, child):for point in range(self.password_size):if np.random.rand() < self.mutate_rate:child[point] = np.random.randint(*self.ascii_bounder)  # choose a random ASCII indexreturn child# 进化def evolution(self):population = self.init_population()for i in range(self.n_iterations):fitness = self.fitness(population)best_person = population[np.argmax(fitness)]best_person_ascii = self.translateDNA(best_person)if i % 10 == 0:print(u'第%-4d次进化后, 基因最好的个体(与欲破解的密码最接近)是: \t %s'% (i, best_person_ascii))if best_person_ascii == self.password:print(u'第%-4d次进化后, 找到了密码: \t %s'% (i, best_person_ascii))breakpopulation = self.select(population)population_copy = population.copy()for parent in population:child = self.create_child(parent, population_copy)child = self.mutate_child(child)parent[:] = childpopulation = populationdef main():password = 'I love you!'     # 要破解的密码ga = GeneticAlgorithm(cross_rate=0.8, mutation_rate=0.01, n_population=300, n_iterations=500, password=password)ga.evolution()if __name__ == '__main__':main()

• 1
• 2
• 3
• 4
• 5
• 6
• 7
• 8
• 9
• 10
• 11
• 12
• 13
• 14
• 15
• 16
• 17
• 18
• 19
• 20
• 21
• 22
• 23
• 24
• 25
• 26
• 27
• 28
• 29
• 30
• 31
• 32
• 33
• 34
• 35
• 36
• 37
• 38
• 39
• 40
• 41
• 42
• 43
• 44
• 45
• 46
• 47
• 48
• 49
• 50
• 51
• 52
• 53
• 54
• 55
• 56
• 57
• 58
• 59
• 60
• 61
• 62
• 63
• 64
• 65
• 66
• 67
• 68
• 69
• 70
• 71
• 72
• 73
• 74
• 75
• 76
• 77
• 78
• 79
• 80
• 81
• 82
• 83
• 84
• 85
• 86
• 87
• 88
• 89
• 90
• 91
• 92
• 93
• 94
• 95
• 96
• 97
• 98
• 99
• 100
• 101
• 102
• 103