遗传算法pyrhin

遗传算法（Genetic Algorithm）是一种优化问题的求解方法，它模拟了生物进化中的遗传和适应过程。下面是一个简单的遗传算法的Python实现示例：

import random

# 遗传算法参数
POPULATION_SIZE = 50  # 种群大小
P_CROSSOVER = 0.9  # 交叉概率
P_MUTATION = 0.1  # 变异概率
MAX_GENERATION = 100  # 最大迭代次数

# 优化目标函数（这里以求解最大值为例）
def fitness_function(individual):
    return sum(individual)

# 初始化种群
def initialize_population():
    population = []
    for _ in range(POPULATION_SIZE):
        individual = [random.randint(0, 1) for _ in range(10)]  # 假设染色体长度为10
        population.append(individual)
    return population

# 选择操作（这里使用轮盘赌选择）
def selection(population, fitness_values):
    total_fitness = sum(fitness_values)
    probabilities = [fitness / total_fitness for fitness in fitness_values]
    selected = random.choices(population, probabilities, k=POPULATION_SIZE)
    return selected

# 交叉操作（这里使用单点交叉）
def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1) - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作（这里使用单点变异）
def mutation(individual):
    mutation_point = random.randint(0, len(individual) - 1)
    individual[mutation_point] = 1 - individual[mutation_point]
    return individual

# 遗传算法主程序
def genetic_algorithm():
    population = initialize_population()
    
    for generation in range(MAX_GENERATION):
        fitness_values = [fitness_function(individual) for individual in population]
        best_individual = population[fitness_values.index(max(fitness_values))]
        
        if generation % 10 == 0:
            print("Generation:", generation, "Best Fitness:", max(fitness_values))
        
        if max(fitness_values) == sum(best_individual):
            break
        
        selected = selection(population, fitness_values)
        next_population = []
        
        while len(next_population) < POPULATION_SIZE:
            parent1, parent2 = random.sample(selected, 2)
            
            if random.random() < P_CROSSOVER:
                offspring1, offspring2 = crossover(parent1, parent2)
            else:
                offspring1, offspring2 = parent1[:], parent2[:]
                
            if random.random() < P_MUTATION:
                offspring1 = mutation(offspring1)
            if random.random() < P_MUTATION:
                offspring2 = mutation(offspring2)
                
            next_population.extend([offspring1, offspring2])
            
        population = next_population
    
    return best_individual

# 测试
best_solution = genetic_algorithm()
print("Best Solution:", best_solution)

上述代码是一个基本的遗传算法实现，其中包括了种群初始化、选择、交叉、变异等基本操作。你可以根据具体问题对代码进行适当修改和扩展。