python实现自适应参数的遗传算法,给出实现代码
时间: 2024-01-16 10:05:05 浏览: 26
以下是一个简单的 Python 遗传算法实现,使用了自适应参数(adaptive parameter)的方法:
```python
import random
import math
# 定义适应度函数
def fitness(x):
return x * math.sin(10 * math.pi * x) + 2.0
# 定义遗传算法
def ga():
# 初始化
pop_size = 50
chrom_len = 20
pc = 0.8
pm = 0.01
max_iter = 100
best_fitness = -9999
best_chromosome = []
# 计算初始适应度
population = [[random.randint(0, 1) for _ in range(chrom_len)] for _ in range(pop_size)]
fitness_list = [fitness(int(''.join(map(str, chromosome)), 2)) for chromosome in population]
# 进化
for i in range(max_iter):
# 选择
sum_fitness = sum(fitness_list)
probability = [fitness_list[j] / sum_fitness for j in range(pop_size)]
cumulative_probability = [sum(probability[:k+1]) for k in range(pop_size)]
new_population = []
for j in range(pop_size):
r = random.random()
for k in range(pop_size):
if r <= cumulative_probability[k]:
new_population.append(population[k])
break
# 交叉
for j in range(0, pop_size, 2):
if random.random() < pc:
cpoint = random.randint(1, chrom_len-1)
temp1 = new_population[j][:cpoint] + new_population[j+1][cpoint:]
temp2 = new_population[j+1][:cpoint] + new_population[j][cpoint:]
new_population[j], new_population[j+1] = temp1, temp2
# 变异
for j in range(pop_size):
if random.random() < pm:
mpoint = random.randint(0, chrom_len-1)
new_population[j][mpoint] = 1 - new_population[j][mpoint]
# 计算适应度
new_fitness_list = [fitness(int(''.join(map(str, chromosome)), 2)) for chromosome in new_population]
# 更新参数
if max(new_fitness_list) > best_fitness:
best_fitness = max(new_fitness_list)
best_chromosome = new_population[new_fitness_list.index(best_fitness)]
pc = min(pc + 0.1, 0.95)
pm = max(pm - 0.01, 0.01)
else:
pc = max(pc - 0.1, 0.2)
pm = min(pm + 0.01, 0.5)
# 更新种群
population = new_population
fitness_list = new_fitness_list
return best_fitness, best_chromosome
# 测试
print(ga())
```
在这个实现中,我们先定义了一个适应度函数 `fitness`,接着定义了遗传算法 `ga`。在 `ga` 中,我们初始化了一些参数,如种群大小 `pop_size`、染色体长度 `chrom_len`、交叉概率 `pc`、变异概率 `pm` 和最大迭代次数 `max_iter` 等。然后,我们随机生成了一个初始种群,并计算了每个个体的适应度。接着,我们进行 `max_iter` 次迭代,每次迭代分别进行选择、交叉和变异操作。在每次迭代中,我们还会更新交叉和变异概率 `pc` 和 `pm`,以使算法更加自适应。最后,算法返回最优个体的适应度和染色体。
需要注意的是,这个实现中的自适应参数方法只是一种简单的示例,实际应用中可能需要更复杂的方法来确定参数的变化。