给出遗传算法的改进代码
时间: 2023-07-07 14:40:31 浏览: 45
以下是一个遗传算法的改进代码,其中包括了两个改进措施:精英保留和自适应变异率。
```python
import random
# 种群大小
POPULATION_SIZE = 100
# 基因长度
GENE_LENGTH = 20
# 最大迭代次数
MAX_ITERATION = 100
# 精英保留比例
ELITE_RATIO = 0.1
# 自适应变异率参数
ADAPTIVE_MUTATION_PARAM = 0.5
# 个体类
class Individual:
def __init__(self, gene):
self.gene = gene
self.fitness = self.calculate_fitness()
# 计算适应度
def calculate_fitness(self):
return sum(self.gene)
# 交叉操作
def crossover(self, other):
crossover_point = random.randint(1, GENE_LENGTH - 1)
new_gene = self.gene[:crossover_point] + other.gene[crossover_point:]
return Individual(new_gene)
# 变异操作
def mutate(self, mutation_rate):
for i in range(GENE_LENGTH):
if random.random() < mutation_rate:
self.gene[i] = 1 - self.gene[i]
# 初始化种群
population = []
for i in range(POPULATION_SIZE):
gene = [random.randint(0, 1) for _ in range(GENE_LENGTH)]
individual = Individual(gene)
population.append(individual)
# 迭代
for iteration in range(MAX_ITERATION):
# 计算种群适应度总和
fitness_sum = sum(individual.fitness for individual in population)
# 计算每个个体被选择的概率
selection_probs = [individual.fitness / fitness_sum for individual in population]
# 选择精英个体
elite_size = int(ELITE_RATIO * POPULATION_SIZE)
elite_individuals = sorted(population, key=lambda individual: individual.fitness, reverse=True)[:elite_size]
# 生成新的种群
new_population = []
# 交叉操作
for _ in range(POPULATION_SIZE - elite_size):
# 选择两个个体
selected_indices = random.choices(range(POPULATION_SIZE), weights=selection_probs, k=2)
selected_individuals = [population[index] for index in selected_indices]
# 进行交叉操作
new_individual = selected_individuals[0].crossover(selected_individuals[1])
new_population.append(new_individual)
# 变异操作
for individual in new_population:
mutation_rate = ADAPTIVE_MUTATION_PARAM * (1 - individual.fitness / GENE_LENGTH)
individual.mutate(mutation_rate)
# 将精英个体加入新种群
new_population += elite_individuals
# 更新种群
population = new_population
# 输出最优解
best_individual = sorted(population, key=lambda individual: individual.fitness, reverse=True)[0]
print('Best solution:', best_individual.gene)
print('Fitness:', best_individual.fitness)
```
上述代码中,`ELITE_RATIO` 表示精英保留比例,即每一代中最优个体的比例;`ADAPTIVE_MUTATION_PARAM` 表示自适应变异率参数,它会随着种群适应度的提高而降低变异率,从而增加种群的多样性。
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