目录1、概述

遗传算法的知识点已经梳理完了，现在直接上代码：

2、遗传算法易懂代码（1）代码

#遗传算法：import numpy as npimport matplotlib.pyplot as pltdef fitness(x):    return x+16*np.sin(5*x)+10*np.cos(4*x)class individual:    def __init__(self):        self.x=0        self.fitness=0    def __eq__(self,other):        self.x=other.x        self.fitness=other.fitnessdef initPopulation(POP,N):    for i in range(N):        ind=individual()        ind.x=np.random.uniform(-10,10)        ind.fitness=fitness(ind.x)        POP.append(ind)def selection(N):    return np.random.choice(N,2)def crossover(parent1,parent2):    child1,child2=individual(),individual()    child1.x=0.9*parent1.x+0.1*parent2.x    child2.x=0.1*parent2.x+0.9*parent2.x    child1.fitness=fitness(child1.x)    child2.fitness=fitness(child2.x)    return child1,child2def mutation(POP):    ind=np.random.choice(POP)    ind.x=np.random.uniform(-10,10)    ind.fitness=fitness(ind.x)def implement():    N=40    iter_N=400    POP=[]    initPopulation(POP,N)    for it in range(iter_N):        a,b=selection(N)        if np.random.random()<0.65:            child1,child2=crossover(POP[a],POP[b])            new=sorted([POP[a],POP[b],child1,child2],key=lambda ind:ind.fitness,reverse=True)            POP[a],POP[b]=new[0],new[1]        if np.random.random()<0.1:            mutation(POP)    return POPif __name__ =='__main__':    pop=implement()def func(x):    return x+16*np.sin(5*x)+10*np.cos(4*x)x=np.linspace(-10,10,100000)y=func(x)scatter_x=np.array([ind.x for ind in pop])scatter_y=np.array([ind.fitness for ind in pop])best=sorted(pop,key=lambda pop: pop.fitness,reverse=True)[0]print('best_y',best.x)print('best_y',best.fitness)plt.plot(x,y)plt.scatter(best.x,best.fitness,c='g',label='best point')plt.legend()plt.show()

import numpy as npimport matplotlib.pyplot as pltfrom pylab import *mpl.rcParams['font.sans-serif'] = ['SimHei']mpl.rcParams['axes.unicode_minus'] = FalseNP=50                L=2                Pc=0.5                 Pm=0.1                G=100                Xmax=2                Xmin=1                 Ymax=0                Ymin=-1               def calc_f(X):    a = 10    pi = np.pi    x = X[:, 0]    y = X[:, 1]    return 2 * a + x ** 2 - a * np.cos(2 * pi * x) + y ** 2 - a * np.cos(2 * 3.14 * y)def calc_e(X):    sumcost=[]    for i in range(X.shape[0]):        ee = 0        """计算第一个约束的惩罚项"""        e1 = X[i, 0] + X[i, 1] - 6        ee += max(0, e1)        """计算第二个约束的惩罚项"""        e2 = 3 * X[i, 0] - 2 * X[i, 1] - 5        ee += max(0, e2)        sumcost.append(ee)    return sumcost##############遗传操作方法#########def select(X, fitness):    """根据轮盘赌法选择优秀个体"""    fitness = 1 / fitness  # fitness越小表示越优秀，被选中的概率越大，做 1/fitness 处理    fitness = fitness / fitness.sum()  # 归一化    idx = np.array(list(range(X.shape[0])))    X2_idx = np.random.choice(idx, size=X.shape[0], p=fitness)  # 根据概率选择    X2 = X[X2_idx, :]    return X2def crossover(X, c):    """按顺序选择2个个体以概率c进行交叉操作"""    for i in range(0, X.shape[0], 2):        parent1=X[i].copy() #父亲        parent2=X[i + 1].copy()#母亲        # 产生0-1区间的均匀分布随机数，判断是否需要进行交叉替换        if np.random.rand() <= c:            child1=(1-c)*parent1+c*parent2 #这是实数编码 的交叉形式 shape(2,)            #child1=child1.reshape(-1,2)            child2=c*parent1+(1-c)*parent2 #shape(2,)            #child2=child2.reshape(1,2)            #判断个体是否越限            if child1[0]>Xmax or child1[0] < Xmin:                child1[0]=np.random.uniform(Xmin, Xmax)            if child1[1] > Ymax or child1[1] <Ymin:                child1[1] = np.random.uniform(Ymin, Ymax)            if child2[0] > Xmax or child2[0] < Xmin:                child2[0] = np.random.uniform(Xmin, Xmax)            if child2[1] > Ymax or child2[1] < Ymin:                child2[1] = np.random.uniform(Ymin, Ymax)            ######通过比较父辈和子代的适应度值和惩罚项 来决定要不要孩子            X[i, :]=child1            X[i + 1, :]=child2    return Xdef mutation(X, m):    """变异操作"""    for i in range(X.shape[0]):#遍历每一个个体        # 产生0-1区间的均匀分布随机数，判断是否需要进行变异        parent=X[i].copy()#父辈        if np.random.rand() <= m:                child = np.random.uniform(-1,2,(1,2))# 用随机赋值的方式进行变异 得到子代                # 判断个体是否越限                if child[:,0] > Xmax or child[:,0] < Xmin:                    child[:,0] = np.random.uniform(Xmin, Xmax)                if child[:,1] > Ymax or child[:,1] < Ymin:                    child[:,1] = np.random.uniform(Ymin, Ymax)                ######通过比较父辈和子代的适应度值和惩罚项 来决定要不要孩子                X[i]=child    return X#子代和父辈之间的选择操作def update_best(parent,parent_fitness,parent_e,child,child_fitness,child_e):    """        判        :param parent: 父辈个体        :param parent_fitness:父辈适应度值        :param parent_e    ：父辈惩罚项        :param child:  子代个体        :param child_fitness 子代适应度值        :param child_e  ：子代惩罚项        :return: 父辈 和子代中较优者、适应度、惩罚项        """    # 规则1，如果 parent 和 child 都没有违反约束，则取适应度小的    if parent_e <= 0.0000001 and child_e <= 0.0000001:        if parent_fitness <= child_fitness:            return parent,parent_fitness,parent_e        else:            return child,child_fitness,child_e    # 规则2，如果child违反约束而parent没有违反约束，则取parent    if parent_e < 0.0000001 and child_e  >= 0.0000001:        return parent,parent_fitness,parent_e    # 规则3，如果parent违反约束而child没有违反约束，则取child    if parent_e >= 0.0000001 and child_e < 0.0000001:        return child,child_fitness,child_e    # 规则4，如果两个都违反约束，则取适应度值小的    if parent_fitness <= child_fitness:        return parent,parent_fitness,parent_e    else:        return child,child_fitness,child_edef ga():    """遗传算法主函数"""    best_fitness = []  # 记录每次迭代的效果    best_xy = []#存放最优xy    f = np.random.uniform(-1, 2, (NP, 2))  # 初始化种群 （生成-1,2之间的随机数）shape (NP,2)    for i in range(G):#遍历每一次迭代        fitness=np.zeros((NP, 1))#存放适应度值        ee=np.zeros((NP, 1)) #存放惩罚项值        parentfit = calc_f(f)#计算父辈目标函数值        parentee = calc_e(f)#计算父辈惩罚项        parentfitness = parentfit + parentee #计算父辈适应度值   适应度值=目标函数值+惩罚项        X2 = select(f, parentfitness)#选择        X3 = crossover(X2, Pc)#交叉        X4 = mutation(X3, Pm)#变异        childfit = calc_f(X4)  # 子代目标函数值        childee = calc_e(X4)  # 子代惩罚项        childfitness = childfit + childee  # 子代适应度值        for j in range(NP):#遍历每一个个体            X4[j],fitness[j],ee[j] = update_best(f[j], parentfitness[j], parentee[j], X4[j], childfitness[j],childee[j])        best_fitness.append(fitness.min())        x, y = X4[fitness.argmin()]        best_xy.append((x, y))        f=X4        # 多次迭代后的最终效果    print("最优值是：%.5f" % best_fitness[-1])    print("最优解是：x=%.5f, y=%.5f" % best_xy[-1])    # 打印效果    plt.plot(best_fitness, color='r')    plt.show()ga()