python写遗传算法

以下是Python中的简单遗传算法实现示例：

import random

# 目标函数，本例为求解x^2的最小值，x的范围在-10到10之间
def fitness_function(x):
    return x**2

# 生成初始种群
def generate_initial_population(population_size):
    population = []
    for i in range(population_size):
        individual = {'chromosome': random.uniform(-10, 10)}
        individual['fitness'] = fitness_function(individual['chromosome'])
        population.append(individual)
    return population

# 选择操作，采用轮盘赌选择
def selection(population):
    total_fitness = sum([individual['fitness'] for individual in population])
    selection_probs = [(individual['fitness'] / total_fitness) for individual in population]
    selected_individuals = []
    for i in range(len(population)):
        selected_individual = random.choices(population, weights=selection_probs)[0]
        selected_individuals.append(selected_individual)
    return selected_individuals

# 交叉操作，采用单点交叉
def crossover(parent1, parent2):
    offspring1 = parent1['chromosome']
    offspring2 = parent2['chromosome']
    if random.random() < crossover_rate:
        crossover_point = random.randint(1, len(bin(int(parent1['chromosome']))[2:])-1)
        mask = int('1' * crossover_point + '0' * (len(bin(int(parent1['chromosome']))[2:])-crossover_point), 2)
        offspring1 = (parent1['chromosome'] & mask) + (parent2['chromosome'] & ~mask)
        offspring2 = (parent2['chromosome'] & mask) + (parent1['chromosome'] & ~mask)
    offspring1 = {'chromosome': offspring1}
    offspring2 = {'chromosome': offspring2}
    offspring1['fitness'] = fitness_function(offspring1['chromosome'])
    offspring2['fitness'] = fitness_function(offspring2['chromosome'])
    return offspring1, offspring2

# 变异操作，采用随机变异
def mutation(individual):
    mutated_chromosome = individual['chromosome']
    if random.random() < mutation_rate:
        mutation_bit = random.randint(1, len(bin(int(individual['chromosome']))[2:]))
        mask = 1 << (mutation_bit - 1)
        mutated_chromosome = individual['chromosome'] ^ mask
    mutated_individual = {'chromosome': mutated_chromosome}
    mutated_individual['fitness'] = fitness_function(mutated_individual['chromosome'])
    return mutated_individual

# 主程序
population_size = 50
crossover_rate = 0.8
mutation_rate = 0.1
max_generations = 100

# 生成初始种群
population = generate_initial_population(population_size)

# 迭代
for generation in range(max_generations):
    # 选择
    selected_individuals = selection(population)
    # 交叉
    offspring_population = []
    for i in range(0, len(selected_individuals), 2):
        parent1 = selected_individuals[i]
        parent2 = selected_individuals[i+1]
        offspring1, offspring2 = crossover(parent1, parent2)
        offspring_population.append(offspring1)
        offspring_population.append(offspring2)
    # 变异
    mutated_population = [mutation(individual) for individual in offspring_population]
    # 合并种群
    population = selected_individuals + mutated_population
    # 选择最优个体
    best_individual = min(population, key=lambda x: x['fitness'])

print('最优解为：', best_individual['chromosome'])
print('最优解对应的函数值为：', best_individual['fitness'])

请注意，此示例中的目标函数和操作是根据问题而定的，您需要根据您的问题进行相应的修改。此外，此示例还需要其他优化，例如添加停止准则和参数优化等。