画图比较遗传算法优劣的python代码

以下是一个简单的Python程序，用于比较遗传算法和随机搜索在解决函数优化问题中的表现：

import numpy as np
import matplotlib.pyplot as plt

def fitness_func(x):
    # 定义适应度函数，这里使用Rosenbrock函数作为例子
    return sum(100*(x[1:]-x[:-1]**2)**2 +(1-x[:-1])**2)

def init_pop(pop_size, n_dim):
    # 初始化种群
    pop = np.random.rand(pop_size, n_dim)
    return pop

def selection(pop, fitness):
    # 选择操作
    idx = np.random.choice(np.arange(len(pop)), size=len(pop), replace=True, p=fitness/fitness.sum())
    return pop[idx]

def crossover(parents, n_offsprings):
    # 交叉操作
    offsprings = np.zeros((n_offsprings, parents.shape[1]))
    for i in range(n_offsprings):
        idx1, idx2 = np.random.choice(np.arange(len(parents)), size=2, replace=False)
        cross_pt = np.random.randint(0, parents.shape[1])
        offsprings[i, :cross_pt] = parents[idx1, :cross_pt]
        offsprings[i, cross_pt:] = parents[idx2, cross_pt:]
    return offsprings

def mutation(offsprings, mut_rate):
    # 变异操作
    for i in range(len(offsprings)):
        if np.random.rand() < mut_rate:
            mut_pt = np.random.randint(0, offsprings.shape[1])
            offsprings[i, mut_pt] = np.random.rand()
    return offsprings

def ga(pop_size, n_dim, n_gen, mut_rate):
    # 遗传算法主函数
    pop = init_pop(pop_size, n_dim)
    best_fitness = []
    for i in range(n_gen):
        fitness = np.array([fitness_func(x) for x in pop])
        best_fitness.append(fitness.min())
        parents = selection(pop, fitness)
        offsprings = crossover(parents, len(pop)-len(parents))
        offsprings = mutation(offsprings, mut_rate)
        pop = np.vstack([parents, offsprings])
    return best_fitness

def random_search(n_trials, n_dim):
    # 随机搜索主函数
    best_fitness = []
    for i in range(n_trials):
        x = np.random.rand(n_dim)
        best_fitness.append(fitness_func(x))
    return best_fitness

if __name__ == '__main__':
    np.random.seed(0)
    pop_size = 50
    n_dim = 10
    n_gen = 100
    mut_rate = 0.1
    n_trials = 500

    ga_results = ga(pop_size, n_dim, n_gen, mut_rate)
    rs_results = random_search(n_trials, n_dim)

    plt.plot(ga_results, label='GA')
    plt.plot(rs_results, label='Random Search')
    plt.legend()
    plt.xlabel('Generation/Trial')
    plt.ylabel('Best Fitness')
    plt.show()

上述代码中，首先定义了一个Rosenbrock函数作为优化目标，然后实现了遗传算法和随机搜索两个函数。在主函数中，通过调用这两个函数来得到优化过程中的最佳适应度值。最后，使用matplotlib库将结果可视化。