请你以python为语言，写一段能跑通的遗传算法代码，并将代码的应用进行文字说明

在Python中，遗传算法是一种模拟自然选择过程的优化算法，通常用于解决复杂的搜索问题。下面是一个简单的遗传算法示例，我们将尝试优化一个函数，使其最小化一个特定的目标函数。这里我们使用了经典的人工鱼群（Swarm Optimization）作为遗传算法的一个实例：

import numpy as np
import random

# 定义目标函数，这里以简单的一维函数为例
def fitness_function(x):
    return x**2 + 10 * np.sin(x)

# 遗传算法设置
pop_size = 50        # 种群大小
num_generations = 100  # 迭代次数
gene_length = 1       # 每个个体的基因长度
mutation_rate = 0.01  # 变异概率
crossover_probability = 0.8  # 交叉概率

# 初始化种群
def initialize_population(pop_size):
    return [np.random.uniform(-10, 10, gene_length) for _ in range(pop_size)]

# 计算适应度
def calculate_fitness(population):
    return [fitness_function(individual) for individual in population]

# 选择操作
def selection(population, fitness_scores):
    parents = np.random.choice(range(len(population)), size=pop_size, p=fitness_scores / sum(fitness_scores))
    return population[parents]

# 交叉操作
def crossover(parents, crossover_probability):
    offspring = []
    for i in range(0, len(parents), 2):
        if random.random() < crossover_probability:
            point = random.randint(0, gene_length - 1)
            child1 = np.concatenate((parents[i][:point], parents[i+1][point:]))
            child2 = np.concatenate((parents[i+1][:point], parents[i][point:]))
        else:
            child1 = parents[i]
            child2 = parents[i+1]
        offspring.extend([child1, child2])
    return offspring

# 变异操作
def mutation(offspring, mutation_rate):
    mutated_offspring = []
    for individual in offspring:
        for i in range(gene_length):
            if random.random() < mutation_rate:
                individual[i] += random.uniform(-1, 1)
        mutated_offspring.append(individual)
    return mutated_offspring

def genetic_algorithm():
    population = initialize_population(pop_size)
    for generation in range(num_generations):
        fitness_scores = calculate_fitness(population)
        elite = population[np.argsort(fitness_scores)[:pop_size // 4]]  # 保留精英
        population = selection(elite + population, fitness_scores)  # 更新种群
        population = crossover(population, crossover_probability)
        population = mutation(population, mutation_rate)
    best_individual = population[np.argmin(calculate_fitness(population))]
    print(f"最佳解：{best_individual}, 最优值：{fitness_function(best_individual)}")

genetic_algorithm()

这段代码首先定义了一个简单的单峰函数`fitness_function`，然后创建一个遗传算法流程，包括初始化种群、计算适应度、选择、交叉和变异等步骤。经过多次迭代后，它会找到该一维空间内使目标函数最小的解。