下面是一个使用Python实现遗传算法的简单示例：
import random
# 定义适应度函数（目标函数）
def fitness_function(chromosome):
x = chromosome[0]
return x**2 + 4*x - 10
# 创建种群
def create_population(population_size, chromosome_size):
population = []
for _ in range(population_size):
chromosome = [random.randint(0, 1) for _ in range(chromosome_size)]
population.append(chromosome)
return population
# 选择操作
def selection(population):
# 使用轮盘赌选择
total_fitness = sum(fitness_function(chromosome) for chromosome in population)
probabilities = [fitness_function(chromosome) / total_fitness for chromosome in population]
selected_chromosomes = random.choices(population, probabilities, k=len(population))
return selected_chromosomes
# 交叉操作
def crossover(parent1, parent2):
# 单点交叉
crossover_point = random.randint(1, len(parent1)-1)
offspring1 = parent1[:crossover_point] + parent2[crossover_point:]
offspring2 = parent2[:crossover_point] + parent1[crossover_point:]
return offspring1, offspring2
# 变异操作
def mutation(chromosome, mutation_rate):
# 位翻转变异
mutated_chromosome = chromosome.copy()
for i in range(len(mutated_chromosome)):
if random.random() < mutation_rate:
mutated_chromosome[i] = 1 - mutated_chromosome[i]
return mutated_chromosome
# 遗传算法主程序
def genetic_algorithm(population_size, chromosome_size, generations, mutation_rate):
population = create_population(population_size, chromosome_size)
for _ in range(generations):
selected_population = selection(population)
new_population = []
while len(new_population) < population_size:
parent1, parent2 = random.choices(selected_population, k=2)
offspring1, offspring2 = crossover(parent1, parent2)
mutated_offspring1 = mutation(offspring1, mutation_rate)
mutated_offspring2 = mutation(offspring2, mutation_rate)
new_population.extend([mutated_offspring1, mutated_offspring2])
population = new_population
best_chromosome = max(population, key=fitness_function)
best_fitness = fitness_function(best_chromosome)
return best_chromosome, best_fitness
# 测试遗传算法
population_size = 50
chromosome_size = 10
generations = 100
mutation_rate = 0.01
best_chromosome, best_fitness = genetic_algorithm(population_size, chromosome_size, generations, mutation_rate)
print("Best Chromosome:", best_chromosome)
print("Best Fitness:", best_fitness)
这个示例实现了一个简单的遗传算法来求解优化问题，其中适应度函数为 x**2 + 4*x - 10。遗传算法的主要步骤包括创建种群、选择、交叉和变异。通过迭代进化多代，逐渐找到更优的染色体作为解。在每一代中，使用选择操作选择优秀的个体，然后进行交叉和变异生成新的后代。
请注意，此示例是一个通用的遗传算法框架，可以根据不同的问题进行调整和扩展。您可以根据自己的优化目标和约束条件定义适应度函数，并根据需要调整其他参数和操作。
