求解fsp的遗传算法python代码

FSP（Flexible Single Parent）是遗传算法（Genetic Algorithm, GA）的一种变体，它在选择操作中采用了一种灵活的方式，即单亲选择，而不是传统的双亲交叉。在Python中实现FSP遗传算法，你可以使用Scikit-Optimize库或者自定义实现基本的GA流程，包括编码、初始化、选择、交叉、变异和适应度评估等步骤。

以下是一个简单的FSP遗传算法的Python代码示例：

import random
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier

# 假设你有一个分类任务，特征集和目标变量
X, y = load_data()
num_features = X.shape

# 1. 初始化
def init_population(size, num_features):
    population = [random.random_sample(num_features) for _ in range(size)]
    return population

# 2. 编码
def encode_solution(solution):
    # 这里假设solution是一个特征向量，可能需要转换为模型的输入格式
    return solution

# 3. FSP选择
def fsp_selection(population, fitness):
    selected = [population]  # 从第一个个体开始
    for i in range(1, len(population)):
        best_idx = max(range(i), key=lambda j: fitness[j])
        selected.append(population[best_idx])
    return selected

# 4. 交叉和变异
def crossover_and_mutate(selected, mutation_rate):
    offspring = []
    for parent1, parent2 in zip(selected[::2], selected[1::2]):
        child = crossover(parent1, parent2)
        child = mutate(child, mutation_rate)
        offspring.append(child)
    return offspring

# 5. 适应度评估
def evaluate_fitness(solution, X, y):
    model = RandomForestClassifier()  # 使用随机森林作为模型
    model.fit(encode_solution(solution), y)
    predictions = model.predict(X)
    return accuracy_score(y, predictions)

# 示例执行过程
pop_size = 100
num_generations = 100
mutation_rate = 0.1
population = init_population(pop_size, num_features)
fitness = [evaluate_fitness(sol, X, y) for sol in population]

for _ in range(num_generations):
    selected = fsp_selection(population, fitness)
    offspring = crossover_and_mutate(selected, mutation_rate)
    population = selected + offspring
    # 更新适应度
    fitness = [evaluate_fitness(sol, X, y) for sol in population]

# 最佳解决方案
best_solution = population[fitness.index(max(fitness))]