python实现遗传算法实例

以下是一个简单的 Python 遗传算法实现示例：

import random

# 定义遗传算法参数
POPULATION_SIZE = 100  # 种群大小
GENERATIONS = 50  # 迭代次数
MUTATION_RATE = 0.1  # 突变率

# 定义问题
OBJECTIVE_FUNCTION = lambda x: sum(x)  # 目标函数
NUM_VARIABLES = 5  # 变量数量
VARIABLE_RANGE = (-5, 5)  # 变量范围

# 初始化种群
population = []
for i in range(POPULATION_SIZE):
    individual = [random.uniform(*VARIABLE_RANGE) for _ in range(NUM_VARIABLES)]
    population.append(individual)

# 迭代
for generation in range(GENERATIONS):
    # 评估适应度
    fitness = [OBJECTIVE_FUNCTION(individual) for individual in population]

    # 选择
    total_fitness = sum(fitness)
    probabilities = [f / total_fitness for f in fitness]
    selected_indices = random.choices(range(len(population)), weights=probabilities, k=len(population))
    selected_population = [population[i] for i in selected_indices]

    # 交叉
    new_population = []
    for i in range(0, len(selected_population), 2):
        parent1 = selected_population[i]
        parent2 = selected_population[i+1]
        child1 = []
        child2 = []
        for j in range(NUM_VARIABLES):
            if random.random() < 0.5:
                child1.append(parent1[j])
                child2.append(parent2[j])
            else:
                child1.append(parent2[j])
                child2.append(parent1[j])
        new_population.append(child1)
        new_population.append(child2)

    # 突变
    for i in range(len(new_population)):
        for j in range(NUM_VARIABLES):
            if random.random() < MUTATION_RATE:
                new_population[i][j] = random.uniform(*VARIABLE_RANGE)

    population = new_population

# 输出最优解
best_individual = max(population, key=OBJECTIVE_FUNCTION)
print("Best individual:", best_individual)
print("Objective value:", OBJECTIVE_FUNCTION(best_individual))

该示例实现了一个遗传算法来求解一个简单的多元函数最大值问题。在每一代中，种群通过选择、交叉和突变产生新一代个体，并且适应度高的个体具有更高的概率被选择。该算法最终输出最优解和对应的目标函数值。