遗传算法的Python实现

import numpy as np
import matplotlib.pyplot as plt

DNA_SIZE=10
POP_SIZE=100
CROSS_RATE=0.8
MUTATION_RATE=0.003
N_GENERATIONS=200
X_BOUND=[0,5]

#定义目标函数
def F(x):
    return np.sin(10*x)*x+np.cos(2*x)*x

#计算适应度,并使其大于0
def get_fitness(pred):
    return pred+1e-3-np.min(pred)

#将二进制串的DNA转为10进制数并限制范围在0到5之间
def translateDNA(population):
    return population.dot(2**np.arange(DNA_SIZE)[::-1])/float(2**DNA_SIZE)*X_BOUND[1]

#根据种群中个体适应度做自然选择
def select(population,fitness):
    idx=np.random.choice(np.arange(POP_SIZE),size=POP_SIZE,replace=True,p=fitness/fitness.sum())
    return population[idx]

#交叉
def crossover(parent,population):#其实，population 与parent是一个东西
    if np.random.rand()<CROSS_RATE:
        i_=np.random.randint(0,POP_SIZE,size=1)#选择母本
        cross_points=np.random.randint(0,2,size=DNA_SIZE).astype(np.bool)
        parent[cross_points]=population[i_,cross_points]
    return parent#此时的parent已经是child了

#变异
def mutate(child):
    for point in range(DNA_SIZE):
        if np.random.rand()<MUTATION_RATE:
            child[point]=1 if child[point]==0 else 0
    return child

if __name__ == '__main__':
    plt.ion()       # something about plotting
    x = np.linspace(*X_BOUND, 200)
    plt.plot(x, F(x))
    
    population=np.random.randint(2,size=(POP_SIZE,DNA_SIZE))
    for _ in range(N_GENERATIONS):
        F_values=F(translateDNA(population))

        # something about plotting
        if 'sca' in globals(): sca.remove()
        sca = plt.scatter(translateDNA(population), F_values, s=200, lw=0, c='red', alpha=0.5); plt.pause(0.05)

        fitness=get_fitness(F_values)
        print("Most fitted DNA: ", population[np.argmax(fitness), :])
        population=select(population,fitness)
        pop_copy=population.copy()
        for parent in population:
            child=crossover(parent,pop_copy)
            child=mutate(child)
            parent[:]=child#child覆盖parent，相当于进化了
    plt.ioff()
    plt.show()

参考：

https://github.com/MorvanZhou/Evolutionary-Algorithm/blob/master/tutorial-contents/Genetic%20Algorithm/Genetic%20Algorithm%20Basic.py