) i=1 sum=0 _____________________________________ sum+=i i+=2 print(‘1+3+5+……+97+99=’,sum)
时间: 2023-12-16 22:02:35 浏览: 18
根据题意,这个代码段的作用是累加奇数的和,最终输出总和。代码有些错误,应该改为:
i = 1
sum = 0
while i <= 99:
sum += i
i += 2
print('1 3 5 …… 97 99 =', sum)
输出结果为:"1 3 5 …… 97 99 = 2500"。
相关问题
partial_sum
partial_sum是C++ STL算法组件中的一个函数,用于计算某个序列局部元素的和。它有多个重载函数,可以根据需要选择适合的函数来使用。要使用partial_sum函数,需要引用头文件numeric。partial_sum函数的定义在numeric头文件中,它有两种语法格式:OutputIterator partial_sum (InputIterator first, InputIterator last, OutputIterator result)和OutputIterator partial_sum (InputIterator first, InputIterator last, OutputIterator result, BinaryOperation binary_op)。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [udimanber add(y,i) partial_sum(i)](https://download.csdn.net/download/mostovoi1234/2986350)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *2* [partial_sum 的使用](https://blog.csdn.net/weixin_44566320/article/details/91355171)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
- *3* [partial_sum函数用法](https://blog.csdn.net/Albdon/article/details/119993932)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 33.333333333333336%"]
[ .reference_list ]
Python_多目标遗传算法
多目标遗传算法是一种优化算法,用于解决多个目标函数的优化问题。在很多实际问题中,我们需要同时优化多个目标函数,但是这些目标函数通常是互相矛盾的,即优化其中一个目标函数会导致其他目标函数的值变差。多目标遗传算法通过维护一组解的集合,称为“种群”,并通过遗传操作(如交叉和变异)来不断改进这个种群,从而逐步逼近最优解的集合,称为“帕累托前沿”。
以下是一个简单的Python多目标遗传算法的示例代码:
```python
import random
# 定义目标函数
def obj_func(x):
return [x[0]**2, (x[0]-2)**2]
# 定义种群大小和变量范围
pop_size = 50
var_range = [-5, 5]
# 初始化种群
pop = [[random.uniform(var_range[0], var_range[1]) for _ in range(1)] for _ in range(pop_size)]
# 迭代次数
num_iter = 100
# 开始迭代
for i in range(num_iter):
# 计算每个个体的目标函数值
obj_vals = [obj_func(x) for x in pop]
# 计算每个个体的适应度值
fitness_vals = []
for j in range(pop_size):
fitness_vals.append(sum([1 for k in range(pop_size) if obj_vals[k][0] <= obj_vals[j][0] and obj_vals[k][1] <= obj_vals[j][1]]))
# 选择操作
new_pop = []
for j in range(pop_size):
parent1 = random.choices(pop, weights=fitness_vals)[0]
parent2 = random.choices(pop, weights=fitness_vals)[0]
child = [(parent1[0] + parent2[0])/2]
new_pop.append(child)
# 变异操作
for j in range(pop_size):
if random.random() < 0.1:
new_pop[j][0] += random.uniform(-0.5, 0.5)
# 更新种群
pop = new_pop
# 输出帕累托前沿
obj_vals = [obj_func(x) for x in pop]
pareto_front = []
for i in range(pop_size):
if sum([1 for j in range(pop_size) if obj_vals[j][0] <= obj_vals[i][0] and obj_vals[j][1] <= obj_vals[i][1]]) == 1:
pareto_front.append(pop[i])
print(pareto_front)
```
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