这次代码的修改点在select函数中，在100个个体中，通过轮盘赌选择出前48个个体，然后再选择出最优个体和最差个体，总共50个，然后再在轮盘赌中后剩下的50个个体进行交叉和变异，返回出50个，这样50+50=100个体作为下一次进化的初始种群，如此不断循环，得出结果。
select如下：
def select(pop, fitness): # nature selection wrt pop's fitness
# fitnew=fitness.copy() #深拷贝
fitnew = fitness
# print('fitness',id(fitness))# 赋值操作在Python里是浅拷贝，两个变量地址一样
# print('fitnew',id(fitnew))
# print(id(fitnew)==id(fitness)) #True
fitnew = fitnew + 1e-3 - np.min(fitnew)
p = (fitnew) / (fitnew.sum())
# print(np.arange(POP_SIZE)) #产生的是一维数组[0 1 2 ...100]
# idx = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True, p=p)
# 从0~POP_SIZE这个序列里随机取样 如果[pop_size]是一维数组，就表示从这个一维数组中随机采样,采size个，上面这行是全采用
idx = choicebyyang(np.arange(POP_SIZE), size=POP_SIZE, replace=True, p=p) #一维数组
bestindex = getbest(pop, fitness) #最优个体下标
worstindex=getworst(pop,fitness) #最差个体下标
# new_idx=[bestindex,worstindex] #<class 'list'>: [0, 1]
# new_idx=[idx]+[bestindex,worstindex] #<class 'list'>: [array([0, 0, 0]), 0, 2]
# new_idx = list(idx) + [bestindex, worstindex] #<class 'list'>: [0, 1, 1, 1, 2]
new_idx=list(idx) #<class 'list'>: [2, 1, 2]
half_pop_idx=new_idx[:POP_SIZE//2-2]
half_pop_idx.append(bestindex)
half_pop_idx.append(worstindex)
half_pop_idx2=new_idx[POP_SIZE//2:]
return pop[half_pop_idx],pop[half_pop_idx2] # 尽量选择适应值高的函数值的个体还有在循环主体有点变化如下：
for _ in range(N_GENERATIONS): # 迭代N代
x, y, z = translateDNA(pop) # 这句代码，我觉得没啥作用
# print(x) #(100,) [4.82264692 4.04610252 4.92107325 4.49556859 3.1322498 3.60757363...] 一维数组100个数据
# pop = np.array(crossover_and_mutation(pop, CROSSOVER_RATE))
# print(pop.dtype)#<class 'numpy.ndarray'> (100, 3) 2 300 float64
fitness = get_fitness(pop)
# print(fitness) #<class 'numpy.ndarray'> (100,) 一维数组 100
pop_half,pop_half2 = select(pop, fitness) # 选择生成新的种群 50行3列
pop_half2=np.array(crossover_and_mutation(pop_half2, CROSSOVER_RATE)) #50行3列
pop=np.vstack((pop_half,pop_half2)) #纵向拼接 1003列完整代码如下：
# x1*x1-x1*x2+x3
import numpy as np
import random
DNA_SIZE = 1
POP_SIZE = 100
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.015
N_GENERATIONS = 200
X_BOUND = [3.0, 5.0] # x1
Y_BOUND = [2.1, 6.7] # x2
Z_BOUND = [1.2, 9.6] # x3
sum=0#测试变量
def F(x, y, z):
val = x * x - x * y + z
# print(val.shape) #(100,) 100 <class 'numpy.ndarray'>
return val
def get_fitness(pop):
x, y, z = translateDNA(pop)
pred = F(x, y, z)
return pred
def translateDNA(pop): # pop表示种群矩阵，一行表示一个二进制编码表示的DNA，矩阵的行数为种群数目
# x_pop = pop[:,0:DNA_SIZE]#这样的写法shape 是(3, 1) 3行1列 ndim维度是2(行，列 矩阵 )
# 也可以认为是二维数组，有3行，每行有1个元素 size为3 [[3.18796615]\n [3.32110516]\n [4.34665405]]
'''因为这样写x_pop = pop[:, 0:DNA_SIZE] shape是(3,1)是二维数组，所以会报"对象太深，无法容纳所需的数组"的错误，
第一种解决方法是进行reshape，比如reshape(3,)即变成了一维数组，元素个数是3个，即语法是x_pop=pop[:,0:DNA_SIZE].reshape(POP_SIZE,)
这时x_pop就变为[4.96893731 3.24515899 3.51500566] 一维数组
第二种方法是在矩阵（二维数组）pop中直接选择某一列元素，比如 pop[:, 0],表示选择pop第0列所有的元素
'''
x_pop = pop[:,0] # 取前DNA_SIZE个列表示x 这样的写法shape是(3,) ndim维度是1 一维数组 ，数组元素有3个 size为3 [4.28040552 3.25412449 4.61336022]
# print(x_pop.shape)
y_pop = pop[:, 1] # 取中间DNA_SIZE个列表示y
z_pop = pop[:, 2] # 取后DNA_SIZE个列表示z
# print(x_pop)
return x_pop, y_pop, z_pop
def mutation(child, MUTATION_RATE=0.003):
if np.random.rand() < MUTATION_RATE: # 以MUTATION_RATE的概率进行变异
mutate_point = np.random.randint(0, DNA_SIZE * 3) # 随机产生一个实数，代表要变异基因的位置
if mutate_point == 0:
child[mutate_point] = np.random.uniform(3.0, 5.0)
elif mutate_point == 1:
child[mutate_point] = np.random.uniform(2.1, 6.7)
else:
child[mutate_point] = np.random.uniform(1.2, 9.6)
def crossover_and_mutation(pop, CROSSOVER_RATE=0.015):
new_pop = []
for father in pop: # 遍历种群中的每一个个体，将该个体作为父亲
child = father # 孩子先得到父亲的全部基因
if np.random.rand() < CROSSOVER_RATE: # 产生子代时不是必然发生交叉，而是以一定的概率发生交叉
# mother = pop[np.random.randint(POP_SIZE)] # 再种群中选择另一个个体，并将该个体作为母亲
mother = pop[np.random.randint(POP_SIZE//2)]
cross_points = np.random.randint(low=0, high=DNA_SIZE * 3) # 随机产生交叉的点
# child[cross_points:] = mother[cross_points:] # 孩子得到位于交叉点后的母亲的基因
child[cross_points] = mother[cross_points]
mutation(child, MUTATION_RATE) # mutation(child,MUTATION_RATE)每个后代有一定的机率发生变异
new_pop.append(child)
return new_pop
def getbest(pop, fitness):
best_indiv = []
fitness = get_fitness(pop)
max_fitness_index = np.argmax(fitness)
pop_copy_max = pop[max_fitness_index]
best_indiv.append(pop_copy_max)
# print(best_indiv)
return max_fitness_index
def getworst(pop,fitness):
worst_indiv = []
fitness = get_fitness(pop)
min_fitness_index = np.argmin(fitness)
pop_copy_min = pop[min_fitness_index]
worst_indiv.append(pop_copy_min)
return min_fitness_index
def choicebyyang(arr, size, replace, p):
for i in range(size):
aa = np.random.rand()
sum = 0
for j in range(size):
sum = sum + p[j] # 累加概率
if sum >= aa:
break
arr[i] = j
return arr
def select(pop, fitness): # nature selection wrt pop's fitness
# fitnew=fitness.copy() #深拷贝
fitnew = fitness
# print('fitness',id(fitness))# 赋值操作在Python里是浅拷贝，两个变量地址一样
# print('fitnew',id(fitnew))
# print(id(fitnew)==id(fitness)) #True
fitnew = fitnew + 1e-3 - np.min(fitnew)
p = (fitnew) / (fitnew.sum())
# print(np.arange(POP_SIZE)) #产生的是一维数组[0 1 2 ...100]
# idx = np.random.choice(np.arange(POP_SIZE), size=POP_SIZE, replace=True, p=p)
# 从0~POP_SIZE这个序列里随机取样 如果[pop_size]是一维数组，就表示从这个一维数组中随机采样,采size个，上面这行是全采用
idx = choicebyyang(np.arange(POP_SIZE), size=POP_SIZE, replace=True, p=p) #一维数组
bestindex = getbest(pop, fitness) #最优个体下标
worstindex=getworst(pop,fitness) #最差个体下标
# new_idx=[bestindex,worstindex] #<class 'list'>: [0, 1]
# new_idx=[idx]+[bestindex,worstindex] #<class 'list'>: [array([0, 0, 0]), 0, 2]
# new_idx = list(idx) + [bestindex, worstindex] #<class 'list'>: [0, 1, 1, 1, 2]
new_idx=list(idx) #<class 'list'>: [2, 1, 2]
half_pop_idx=new_idx[:POP_SIZE//2-2]
half_pop_idx.append(bestindex)
half_pop_idx.append(worstindex)
half_pop_idx2=new_idx[POP_SIZE//2:]
return pop[half_pop_idx],pop[half_pop_idx2] # 尽量选择适应值高的函数值的个体
'''
如果POP_SIZE=3,即种群个数是3，则从交叉，变异后的种群中，选择3个适应值高 pop[idx]=[2 0 0]的新个体去
更新pop种群，之后再进行不断的迭代，直到达到迭代次数终止。
'''
def print_info(pop):
fitness = get_fitness(pop)
max_fitness_index = np.argmax(fitness)
print("max_fitness:", fitness[max_fitness_index])
x, y, z = translateDNA(pop)
print("最优的基因型：", pop[max_fitness_index])
print("(x, y, z):", (x[max_fitness_index], y[max_fitness_index], z[max_fitness_index]))
if __name__ == "__main__":
# for i in range(100):#测试代码
pop1 = np.random.uniform(3.0, 5.0, size=(POP_SIZE, DNA_SIZE)) # matrix (POP_SIZE, DNA_SIZE)
pop2 = np.random.uniform(2.1, 6.7, size=(POP_SIZE, DNA_SIZE))
pop3 = np.random.uniform(1.2, 9.6, size=(POP_SIZE, DNA_SIZE))
# print(type(pop1))# (100,1) 维度是2(行列 矩阵) <class 'numpy.ndarray'>
# pop={pop1,pop2,pop3}
pop = np.hstack((pop1, pop2, pop3))#水平（左右）拼接
# print(pop)
'''
[[3.44603448 4.51707625 7.90178727]
[4.57616299 5.11309286 4.86911781]
[3.24273815 2.9253602 4.45149325]
...
[4.39321276 3.1657492 5.16654786]]
'''
# print(type(pop)) #<class 'numpy.ndarray'> n维数组
# print(pop.shape) #(100,3) 矩阵有 100行，3列
# print(pop.ndim) # 2 因为矩阵有行和列两个维度
# print(pop.size) #300 矩阵共有300个元素
# print(pop.dtype) #float64 矩阵元素类型是float64
for _ in range(N_GENERATIONS): # 迭代N代
x, y, z = translateDNA(pop) # 这句代码，我觉得没啥作用
# print(x) #(100,) [4.82264692 4.04610252 4.92107325 4.49556859 3.1322498 3.60757363...] 一维数组100个数据
# pop = np.array(crossover_and_mutation(pop, CROSSOVER_RATE))
# print(pop.dtype)#<class 'numpy.ndarray'> (100, 3) 2 300 float64
fitness = get_fitness(pop)
# print(fitness) #<class 'numpy.ndarray'> (100,) 一维数组 100
pop_half,pop_half2 = select(pop, fitness) # 选择生成新的种群 50行3列
pop_half2=np.array(crossover_and_mutation(pop_half2, CROSSOVER_RATE)) #50行3列
pop=np.vstack((pop_half,pop_half2)) #纵向（上下）拼接 1003列
print_info(pop)
#测试 运行100次 的最大值平均值代码
# i=i+1
# print(i)
# fitness = get_fitness(pop)
# max_fitness_index = np.argmax(fitness)
# sum=sum+fitness[max_fitness_index]
# print(sum/100.0)
'''#随机数-随机值
for i in range(10):
i=np.random.rand()
print(i)
i=i+1
'''
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