这两句代码什么意思[~,index] = sort(fitness(:,1)); parents = pop(index(1:popSize/2),:);
时间: 2024-05-25 21:10:36 浏览: 11
这两句代码是MATLAB语言中的语句。假设fitness是一个n行1列的矩阵,sort(fitness(:,1))表示按照fitness矩阵第一列的元素值进行升序排列,[~,index]表示将排序后的fitness矩阵第一列的元素值对应的下标保存在index中。其中“~”表示占位符,表示不需要返回的值。parents = pop(index(1:popSize/2),:)表示根据index中排名前popSize/2个元素的下标,从pop矩阵中选取相应的行作为parents矩阵。其中pop矩阵是一个m行n列的矩阵,m表示种群数量,n表示染色体长度。这段代码的作用是根据适应度函数对种群进行选择,选择出适应度较高的个体作为父母亲代进行交叉和变异。
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下面这个代码报错了,应该怎么改: %%Matlab Genetic Algorithm for Sin Prediction clear; clc; %population size Npop=50; %create the population Pop=rand(Npop,1)*2*pi; %define fitness fit=@(x) sin(x); %fitness score score=fit(Pop); %maximum number of generations maxgen=100; %weights w=0.7; %probability p_crossover=0.9; p_mutation=0.2; %loop for number of generations for gen=1:maxgen %ranking %rank the population in descending order [~,rank]=sort(score); %rank the population in ascending order rank=flipud(rank); %normalised rank NormalisedRank=rank/sum(rank); %selection %cumulative sum of the normalised rank cumulativeSum=cumsum(NormalisedRank); %randomly select the two parents %from the populations based on their %normalised rank randnum=rand; parent1=find(cumulativeSum>randnum,1); randnum=rand; parent2=find(cumulativeSum>randnum,1); %crossover %randomly select the crossover point pc=randi([1 Npop-1]); %create the offsprings offspring1=[Pop(parent1,1:pc) Pop(parent2,pc+1:end)]; offspring2=[Pop(parent2,1:pc) Pop(parent1,pc+1:end)]; %perform crossover with a probability if(rand<p_crossover) Pop=[Pop; offspring1; offspring2]; end %mutation %randomly select the point of mutation pm=randi([1 Npop]); %mutate the value under the chosen point Pop(pm)=rand*2*pi; %perform mutation with a probability if (rand<p_mutation) Pop(pm)=rand*2*pi; end %evaluate new population score=fit(Pop); %elitism %sort the population in ascending order %of their fitness score [score,rank]=sort(score); elite=Pop(rank(1),:); Pop(rank(Npop),:)=elite; %replace old population Pop=Pop(1:Npop,:); end %print the best solution disp('Best Solution: '); disp(elite);
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遗传算法求解无约束单目标优化问题变量x1、x2的python代码
以下是一个遗传算法求解无约束单目标优化问题(包含两个变量x1和x2)的Python代码示例:
```python
import random
# 定义目标函数
def fitness(x1, x2):
return (x1 - 2)**2 + (x2 + 3)**2
# 遗传算法参数设置
pop_size = 50 # 种群大小
num_generations = 100 # 迭代次数
mutation_rate = 0.1 # 变异率
crossover_rate = 0.8 # 交叉率
# 生成初始种群
population = []
for i in range(pop_size):
x1 = random.uniform(-10, 10)
x2 = random.uniform(-10, 10)
population.append((x1, x2))
# 遗传算法主循环
for generation in range(num_generations):
# 计算适应度并排序
fitness_scores = [(fitness(x1, x2), x1, x2) for (x1, x2) in population]
fitness_scores.sort(reverse=True)
# 输出当前最优解
print("Generation {}: Best fitness = {:.4f}, Best solution = ({:.4f}, {:.4f})".format(generation, fitness_scores[0][0], fitness_scores[0][1], fitness_scores[0][2]))
# 选择
elite_size = int(pop_size * 0.2) # 精英数量
parents = [(x1, x2) for (_, x1, x2) in fitness_scores[:elite_size]] # 选择精英父母
for i in range(pop_size - elite_size):
parent1 = random.choice(parents)
parent2 = random.choice(parents)
# 交叉
if random.random() < crossover_rate:
x1_child = (parent1[0] + parent2[0]) / 2
x2_child = (parent1[1] + parent2[1]) / 2
else:
x1_child = parent1[0]
x2_child = parent1[1]
# 变异
if random.random() < mutation_rate:
x1_child += random.gauss(0, 1)
x2_child += random.gauss(0, 1)
population[i + elite_size] = (x1_child, x2_child)
# 输出最终结果
fitness_scores = [(fitness(x1, x2), x1, x2) for (x1, x2) in population]
fitness_scores.sort(reverse=True)
print("Final solution: ({:.4f}, {:.4f}), fitness = {:.4f}".format(fitness_scores[0][1], fitness_scores[0][2], fitness_scores[0][0]))
```
在这个代码中,我们首先定义了目标函数 `fitness(x1, x2)`,然后设置了遗传算法的参数,包括种群大小、迭代次数、变异率和交叉率。接着我们生成了初始种群,然后开始遗传算法的主循环。在每一次迭代中,我们计算了种群中每个个体的适应度,并对它们进行排序,然后输出当前最优解。接着进行选择、交叉和变异操作,生成下一代种群,直到迭代次数达到设定值为止。最后输出最终的最优解。
需要注意的是,由于这个问题包含两个变量,因此在代码中需要相应地进行修改,包括生成初始种群、计算适应度、选择、交叉和变异操作等。
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