进化算法 评价指标PD
时间: 2023-12-25 16:26:57 浏览: 39
进化算法的评价指标PD是指“平均适应度的标准差”,它是用来衡量种群的多样性和收敛速度的。PD值越大,说明种群的多样性越高,搜索空间的探索能力越强;PD值越小,说明种群的多样性越低,搜索空间的探索能力越弱,但是收敛速度会更快。因此,PD值需要在多样性和收敛速度之间进行平衡,以获得更好的优化结果。
下面是一个使用Python实现遗传算法并计算PD值的例子:
```python
import random
import numpy as np
# 定义适应度函数
def fitness_func(solution):
return sum(solution)
# 定义遗传算法
def genetic_algorithm(population_size, chromosome_length, fitness_func, selection_func, crossover_func, mutation_func, generations):
# 初始化种群
population = np.zeros((population_size, chromosome_length))
for i in range(population_size):
population[i] = np.random.randint(2, size=chromosome_length)
# 迭代进化
for generation in range(generations):
# 计算适应度
fitness = np.zeros(population_size)
for i in range(population_size):
fitness[i] = fitness_func(population[i])
# 计算PD值
pd = np.std(fitness)
# 选择
parents = selection_func(population, fitness)
# 交叉
offspring = crossover_func(parents)
# 变异
offspring = mutation_func(offspring)
# 合并父代和子代
population = np.vstack((population, offspring))
# 选择新的种群
fitness = np.zeros(population_size)
for i in range(population_size):
fitness[i] = fitness_func(population[i])
idx = np.argsort(fitness)[::-1][:population_size]
population = population[idx]
# 返回最优解和PD值
fitness = np.zeros(population_size)
for i in range(population_size):
fitness[i] = fitness_func(population[i])
idx = np.argsort(fitness)[::-1][0]
best_solution = population[idx]
best_fitness = fitness[idx]
pd = np.std(fitness)
return best_solution, best_fitness, pd
# 定义选择函数
def roulette_wheel_selection(population, fitness):
idx = np.random.choice(len(population), size=len(population), replace=True, p=fitness/fitness.sum())
parents = population[idx]
return parents
# 定义交叉函数
def uniform_crossover(parents):
offspring = np.zeros_like(parents)
for i in range(len(parents)):
for j in range(len(parents[i])):
if np.random.rand() < 0.5:
offspring[i][j] = parents[i][j]
else:
offspring[i][j] = parents[np.random.randint(len(parents))][j]
return offspring
# 定义变异函数
def flip_mutation(offspring):
for i in range(len(offspring)):
for j in range(len(offspring[i])):
if np.random.rand() < 0.01:
offspring[i][j] = 1 - offspring[i][j]
return offspring
# 运行遗传算法并计算PD值
best_solution, best_fitness, pd = genetic_algorithm(population_size=100, chromosome_length=10, fitness_func=fitness_func, selection_func=roulette_wheel_selection, crossover_func=uniform_crossover, mutation_func=flip_mutation, generations=100)
print("Best solution:", best_solution)
print("Best fitness:", best_fitness)
print("PD value:", pd)
```
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