帮我生成上面两个代码的完整代码

生成完整的代码涉及到很多细节，特别是因为遗传算法的具体实现依赖于你使用的库和编程环境。这里我将以Python为例，假设你已经安装了`numpy`和`matplotlib`库，并且使用了一个名为`GeneticAlgorithm`的遗传算法类。这个例子并不包含所有细节，因为真实代码可能相当复杂，但可以提供一个基本结构：

**对于（1）不同初始范围的适应度图：**

import numpy as np
import matplotlib.pyplot as plt
from your_library import GeneticAlgorithm  # 假设这是你的遗传算法类

populations_and_ranges = [(np.arange(1, 1.11, 0.01), "1 to 1.1"),
                          (np.linspace(1, 100, num=100), "1 to 100"),
                          (np.arange(1, 2.01, 0.01), "1 to 2")]

best_fitnesses = {}

for initial_range, label in populations_and_ranges:
    ga = GeneticAlgorithm(population_size, initial_range, ...,
                          max_generations, time_limit, fitness_limit)
    best_fitness = [ga.run() for _ in range(max_iterations)]
    best_fitnesses[label] = best_fitness
    plt.plot(range(1, len(best_fitness)+1), best_fitness, label=label)

plt.xlabel('Iterations')
plt.ylabel('Best Fitness')
plt.legend()
plt.show()

**对于（2）不同交叉概率的适应度图：**

import itertools

crossing_fractions = [0, 0.8, 1]
plots = []

for crossover_fraction in crossing_fractions:
    ga.crossover_fraction = crossover_fraction

    if crossover_fraction == 0:  # 无交叉
        ga.do_crossover = lambda pop: pop
    elif crossover_fraction == 1:  # 全部交叉
        ga.do_crossover = ...  # 实现单点交叉

    best_fitnesses = []
    for _ in range(max_iterations):
        best_fitness = ga.run()
        best_fitnesses.append(best_fitness)
    plots.append((best_fitnesses, f"Crossover Fraction: {crossover_fraction}"))

fig, axs = plt.subplots(len(crossing_fractions), sharex=True, figsize=(10, 15))
for ax, (best_fit, label) in zip(axs, plots):
    ax.plot(range(1, len(best_fit)+1), best_fit, label=label)
    ax.set_ylabel(f'Best Fitness ({label})')

axs[-1].set_xlabel('Iterations')
axs[0].legend()

plt.tight_layout()
plt.show()

请替换`your_library`和缺失的部分（如初始化、交叉、变异的具体实现等）。同时，你需要确保已正确导入所需库并根据实际需求修改参数。这两个例子展示了如何在每次循环中创建一个新实例，并根据给定的条件运行遗传算法，然后绘制结果。