def mutation(population): for i in range(POP_SIZE): if np.random.rand() < MUTATION_RATE: #以一定的变异率进行变异 j = np.random.randint(0, 2, size=1) mutation = (np.random.rand()-0.5)*0.1 #变异值为在[-0.05,0.05]内的随机数 population[i, j] += mutation population[i, j] = np.clip(population[i, j], *X_BOUND[j]) #将参数限制在参数范围内 return population详细解释代码
时间: 2024-04-14 14:32:17 浏览: 23
这段代码实现了遗传算法中的变异操作。变异是为了保持种群的多样性,避免陷入局部最优解。
输入参数 population 是一个二维数组,表示一个种群,其中每一行代表一个个体(染色体)。POP_SIZE 是种群的大小,MUTATION_RATE 是变异率。X_BOUND 是一个二维数组,表示每个参数的取值范围。
代码中的 for 循环遍历种群中的每一个个体。通过 np.random.rand() 生成一个随机数,如果该随机数小于变异率 MUTATION_RATE,就进行变异操作。
接下来,通过 np.random.randint(0, 2, size=1) 生成一个随机数 j,用于选择要变异的参数。
然后,通过 (np.random.rand()-0.5)*0.1 生成一个在[-0.05,0.05]内的随机数 mutation,表示变异的大小。
将个体 i 的第 j 个参数加上变异值 mutation,并使用 np.clip() 方法将参数限制在参数范围 X_BOUND[j] 内。
最后,返回经过变异操作后的种群。
这段代码实现了遗传算法中的基本变异操作,通过随机选择个体和参数,并对参数进行随机的微小变化,以增加种群的多样性,并且可能产生更好的后代个体。
相关问题
请在不影响结果的条件下改变代码的样子:import numpy as np import matplotlib.pyplot as plt x1len = 21 x2len = 18 LEN = x1len + x2len POPULATION_SIZE = 100 GENERATIONS = 251 CROSSOVER_RATE = 0.7 MUTATION_RATE = 0.3 pop = np.random.randint(0,2,size=(POPULATION_SIZE,LEN)) def BinToX(pop): x1 = pop[:,0:x1len] x2 = pop[:,x1len:] x1 = x1.dot(2**np.arange(x1len)[::-1]) x2 = x2.dot(2**np.arange(x2len)[::-1]) x1 = -2.9 + x1*(12 + 2.9)/(np.power(2,x1len)-1) x2 = 4.2 + x2*(5.7 - 4.2)/(np.power(2,x2len)-1) return x1,x2 def func(pop): x1,x2 = BinToX(pop) return 21.5 + x1*np.sin(4*np.pi*x1) + x2*np.sin(20*np.pi*x2) def fn(pop): return func(pop); def selection(pop, fitness): idx = np.random.choice(np.arange(pop.shape[0]), size=POPULATION_SIZE, replace=True, p=fitness/fitness.sum()) return pop[idx] def crossover(IdxP1,pop): if np.random.rand() < CROSSOVER_RATE: C = np.zeros((1,LEN)) IdxP2 = np.random.randint(0, POPULATION_SIZE) pt = np.random.randint(0, LEN) C[0,:pt] = pop[IdxP1,:pt] C[0,pt:] = pop[IdxP2, pt:] np.append(pop, C, axis=0) return def mutation(idx,pop): if np.random.rand() < MUTATION_RATE: mut_index = np.random.randint(0, LEN) pop[idx,mut_index] = 1- pop[idx,mut_index] return best_chrom = np.zeros(LEN) best_score = 0 fig = plt.figure() for generation in range(GENERATIONS): fitness = fn(pop) pop = selection(pop, fitness) if generation%50 == 0: ax = fig.add_subplot(2,3,generation//50 +1, projection='3d', title = "generation:"+str(generation)+" best="+str(np.max(fitness))) x1,x2 = BinToX(pop) z = func(pop) ax.scatter(x1,x2,z) for idx in range(POPULATION_SIZE): crossover(idx,pop) mutation(idx,pop) idx = np.argmax(fitness) if best_score < fitness[idx]: best_score = fitness[idx] best_chrom = pop[idx, :] plt.show() print('最优解:', best_chrom, '| best score: %.2f' % best_score)
import numpy as np
import matplotlib.pyplot as plt
x1len = 21
x2len = 18
LEN = x1len + x2len
POPULATION_SIZE = 100
GENERATIONS = 251
CROSSOVER_RATE = 0.7
MUTATION_RATE = 0.3
pop = np.random.randint(0,2,size=(POPULATION_SIZE,LEN))
def BinToX(pop):
x1 = pop[:,0:x1len]
x2 = pop[:,x1len:]
x1 = -2.9 + x1*(12 + 2.9)/(np.power(2,x1len)-1)
x2 = 4.2 + x2*(5.7 - 4.2)/(np.power(2,x2len)-1)
return x1,x2
def func(x1,x2):
return 21.5 + x1*np.sin(4*np.pi*x1) + x2*np.sin(20*np.pi*x2)
def fn(pop):
x1,x2 = BinToX(pop)
return func(x1,x2);
def selection(pop, fitness):
idx = np.random.choice(np.arange(pop.shape[0]),size=POPULATION_SIZE, replace=True, p=fitness/fitness.sum())
return pop[idx]
def crossover(IdxP1,pop):
if np.random.rand() < CROSSOVER_RATE:
C = np.zeros((1,LEN))
IdxP2 = np.random.randint(0, POPULATION_SIZE)
pt = np.random.randint(0, LEN)
C[0,:pt] = pop[IdxP1,:pt]
C[0,pt:] = pop[IdxP2, pt:]
np.append(pop, C, axis=0)
return
def mutation(idx,pop):
if np.random.rand() < MUTATION_RATE:
mut_index = np.random.randint(0, LEN)
pop[idx,mut_index] = 1- pop[idx,mut_index]
return
best_chrom = np.zeros(LEN)
best_score = 0
fig = plt.figure()
for generation in range(GENERATIONS):
fitness = fn(pop)
pop = selection(pop, fitness)
if generation%50 == 0:
ax = fig.add_subplot(2,3,generation//50 +1, projection='3d',title = "generation:"+str(generation)+" best="+str(np.max(fitness)))
x1,x2 = BinToX(pop)
z = func(x1,x2)
ax.scatter(x1,x2,z)
for idx in range(POPULATION_SIZE):
crossover(idx,pop)
mutation(idx,pop)
idx = np.argmax(fitness)
if best_score < fitness[idx]:
best_score = fitness[idx]
best_chrom = pop[idx, :]
plt.show()
print('最优解:', best_chrom, '| best score: %.2f' % best_score)
请帮我用python写出一个遗传算法求函数最大值,函数值如下:x**2 * np.exp(-x/2) * ((math.sin(5 * (x**2))) ** 2)
好的,下面是一个使用Python实现的遗传算法求解函数最大值的示例代码:
```
import numpy as np
import math
import random
# 定义目标函数
def fitness_func(x):
return x**2 * np.exp(-x/2) * ((math.sin(5 * (x**2))) ** 2)
# 初始化种群
def init_population(pop_size, chrom_length):
population = np.zeros((pop_size, chrom_length))
for i in range(pop_size):
population[i, :] = np.random.permutation(np.arange(chrom_length))
return population
# 计算适应度
def cal_fitness(population):
fitness_value = np.zeros((population.shape[0], 1))
for i in range(population.shape[0]):
chrom = population[i, :]
x = chrom2x(chrom)
fitness_value[i, 0] = fitness_func(x)
return fitness_value
# 将染色体转换为实数
def chrom2x(chrom):
x = np.zeros((1,))
for i in range(chrom.shape[0]):
x += chrom[i] * (2 ** i)
x = x / (2 ** chrom.shape[0] - 1) * 10
return x
# 选择操作
def selection(population, fitness_value):
idx = np.random.choice(population.shape[0], size=2, replace=False)
if fitness_value[idx[0]] > fitness_value[idx[1]]:
return population[idx[0], :]
else:
return population[idx[1], :]
# 交叉操作
def crossover(parent1, parent2):
child = np.zeros(parent1.shape)
cross_point = np.random.randint(1, parent1.shape[0])
child[:cross_point] = parent1[:cross_point]
child[cross_point:] = parent2[cross_point:]
return child
# 变异操作
def mutation(child, mutation_rate):
for i in range(child.shape[0]):
if np.random.rand() < mutation_rate:
child[i] = 1 - child[i]
return child
# 遗传算法主函数
def GA(pop_size, chrom_length, max_iter, mutation_rate):
population = init_population(pop_size, chrom_length)
best_fitness = 0
best_chrom = np.zeros((chrom_length,))
for i in range(max_iter):
fitness_value = cal_fitness(population)
idx = np.argmax(fitness_value)
if fitness_value[idx] > best_fitness:
best_fitness = fitness_value[idx]
best_chrom = population[idx, :]
new_population = np.zeros((pop_size, chrom_length))
for j in range(pop_size):
parent1 = selection(population, fitness_value)
parent2 = selection(population, fitness_value)
child = crossover(parent1, parent2)
child = mutation(child, mutation_rate)
new_population[j, :] = child
population = new_population
best_x = chrom2x(best_chrom)
return best_x, best_fitness
# 调用遗传算法函数求解函数最大值
best_x, best_fitness = GA(pop_size=100, chrom_length=10, max_iter=500, mutation_rate=0.01)
print("最优解:x = %.4f, f(x) = %.4f" % (best_x, best_fitness))
```
这段代码使用遗传算法求解函数 `x**2 * np.exp(-x/2) * ((math.sin(5 * (x**2))) ** 2)` 的最大值,其中 `pop_size` 表示种群大小,`chrom_length` 表示染色体长度,`max_iter` 表示最大迭代次数,`mutation_rate` 表示变异概率。在 `GA` 函数中,我们首先初始化种群,然后循环进行选择、交叉和变异操作,直到达到最大迭代次数或找到最优解为止。
最后输出最优解的 `x` 值和函数最大值。你可以根据需要修改代码中的参数和函数,以获得更好的结果。