遗传算法python实例

以下是一个简单的遗传算法Python实现的示例代码：

import random

# 定义目标函数
def fitness_function(chromosome):
    x = chromosome[0]
    y = chromosome[1]
    return x**2 + y**2

# 初始化种群
def initial_population(population_size, chromosome_length):
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(chromosome_length):
            chromosome.append(random.uniform(-10, 10))
        population.append(chromosome)
    return population

# 选择操作（轮盘赌算法）
def selection(population, fitness_values):
    total_fitness = sum(fitness_values)
    probabilities = [fitness / total_fitness for fitness in fitness_values]
    selected_indices = []
    for i in range(len(population)):
        pick = random.uniform(0, 1)
        current = 0
        for j in range(len(population)):
            current += probabilities[j]
            if current > pick:
                selected_indices.append(j)
                break
    selected_population = [population[i] for i in selected_indices]
    return selected_population

# 交叉操作
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
def mutation(chromosome, mutation_rate):
    for i in range(len(chromosome)):
        if random.uniform(0, 1) < mutation_rate:
            chromosome[i] += random.uniform(-1, 1)
    return chromosome

# 主程序
population_size = 50
chromosome_length = 2
mutation_rate = 0.1
generations = 50

population = initial_population(population_size, chromosome_length)
for generation in range(generations):
    fitness_values = [fitness_function(chromosome) for chromosome in population]
    selected_population = selection(population, fitness_values)
    new_population = []
    while len(new_population) < population_size:
        parent1, parent2 = random.sample(selected_population, 2)
        child1, child2 = crossover(parent1, parent2)
        child1 = mutation(child1, mutation_rate)
        child2 = mutation(child2, mutation_rate)
        new_population.append(child1)
        new_population.append(child2)
    population = new_population

best_chromosome = min(population, key=fitness_function)
best_fitness = fitness_function(best_chromosome)
print("Best solution found: ", best_chromosome)
print("Best fitness found: ", best_fitness)

该示例代码实现了一个简单的遗传算法来最小化二元函数 $x^2 + y^2$。在主程序中，我们首先初始化种群，然后进行一系列进化操作，包括选择、交叉和变异。最后，我们找到最小化目标函数的最佳解并输出结果。

请注意，这只是一个基本的示例代码，如果要使用遗传算法解决更复杂的问题，需要根据具体情况进行调整和优化。