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我已经建立了一个遗传算法，但我觉得我的代码的选择/变异部分有问题。这是我正在谈论的代码的一部分：

#include "stdafx.h"
#include <iostream>
#include <vector>
#include <random>
#include <string>
#include <iomanip>
#include <math.h>

// The random number generator I am using.
std::random_device rd;
std::mt19937 rng(rd());

for (int k = 1; k < population_size; k++)                       // Loop until new population is filled up. K = 1 because first individual has the best genes from last generation.
{
// Calculate total fitness.

double totalfitness = 0;

for (int i = 0; i < population_size; i++)
{
    totalfitness += individuals[i].fitness;
}

// Calculate  relative fitness.

for (int i = 0; i < population_size; i++)
{
    individuals[i].probability = individuals[i].fitness / totalfitness;
}

std::uniform_real_distribution<double> dist2(0.0, 1.0);     // Initiate random number generator to generate a double between 0 and 1.

double rndNumber = dist2(rng);                              // Generate first double
double rndNumber2 = dist2(rng);                             // Generate second double
double offset = 0.0;                                        // Set offset (starting point from which it'll add up probabilities) at 0.
int father = 0;                                             // father is the individual that is picked, initialize at 0.
int mother = 0;

// Pick first parent. Once picked, set the fitness for that individual at 0 so that it can not be picked again.

for (int i = 0; i < population_size; i++)
{
    offset += individuals[i].probability;
    if (rndNumber < offset)
    {
        father = i;
        individuals[i].fitness = 0.0;
        break;
    }
}

offset = 0.0;       // Reset offset to zero because we'll start again for the second parent.
totalfitness = 0;   // Recalculate total fitness using only the remaining individuals and reset total fitness to 0

// Here we recalculate total fitness using only the fitness of the individuals remaining.

for (int i = 0; i < population_size; i++)
{
    totalfitness += individuals[i].fitness;
}

// Then we recalculate probability for the individuals based on the new totalfitness.

for (int i = 0; i < population_size; i++)
{
    individuals[i].probability = individuals[i].fitness / totalfitness;
}

// Then we give back the old fitness to the father/mother

individuals[father].fitness = 1 / (individuals[father].evaluation*individuals[father].evaluation);

// Then pick parent 2.

for (int i = 0; i < population_size; i++)
{
    offset += individuals[i].probability;
    if (rndNumber2 < offset)
    {
        mother = i;
        break;
    }
}

// Having picked father and mother, now the idea is to run a random number generator between 0 and 1 for each gene.
// So if:   father  {5, 8, 9, 3}
//          mother  {1, 5, 2, 6)
//          rndnum  {0, 0, 1, 1}
// then     child   {5, 8, 2, 6}

std::uniform_int_distribution<int> gene_selection(0, 1);        // Initiate random number generator to generate an integer between 0 and 1.

for (int i = 0; i < number_of_variables; i++)
{
    int gene1 = gene_selection(rng);
    if (gene1 == 0)
    {
        new_individuals[k].chromosomes[0].push_back(individuals[father].chromosomes[0].at(i));
    }
    else if (gene1 == 1)
    {
        new_individuals[k].chromosomes[0].push_back(individuals[mother].chromosomes[0].at(i));
    }
}

for (int j = 0; j < number_of_variables; j++)
{
    for (int l = 0; l < 32; l++)
    {
        std::uniform_int_distribution<int> mutation(0, 50);
        int mutation_outcome = mutation(rng);
        if (mutation_outcome == 1)
        {
            new_individuals[k].chromosomes[0].at(j) ^= (1 << l);
            if (new_individuals[k].chromosomes[0].at(j) == 0)
            {
                int new_var = uni(rng);
                new_individuals[k].chromosomes[0].at(j) = new_var;
            }
        }
    }
}
}

// When all new individuals have values, give individuals values of new_individuals and start next round of evaluation.

for (int i = 0; i < population_size; i++)
{
individuals[i] = new_individuals[i];
}

我的代码似乎大部分工作正常。我似乎无法弄清楚为什么它的表现越来越差。最初的几代人似乎经常找到新的、更好的解决方案。几代之后，它停止寻找新的最佳解决方案。

这当然可能是因为没有更好的解决方案，但我同时也在 excel 中进行计算，一个人通常可以通过将其“染色体”之一增加 1 来获得更好的适应性，这通常是1 位更改，因为我通常使用 10000 个人运行此代码，您会说该程序必然会创建一个具有此突变的个人。

我现在已经用调试器多次遍历我的代码，在每一步都显示值等等，但我似乎无法找出哪里出了问题，所以我想我会在这里发布我的代码和看看是否有人能发现我在哪里搞砸了。

仅作记录，该算法只是一个公式求解器。例如，我可以输入 a = 1, b = 6, target = 50, a*gene1 + b *gene2 并且它（理论上）分配的适应度越高，个人越接近获得这个结果。

另外，如果我不得不猜测我搞砸了，我会说它在代码的突变部分：

for (int j = 0; j < number_of_variables; j++)
{
    for (int l = 0; l < 32; l++)
    {
        std::uniform_int_distribution<int> mutation(0, 50);
        int mutation_outcome = mutation(rng);
        if (mutation_outcome == 1)
        {
            new_individuals[k].chromosomes[0].at(j) ^= (1 << l);
            if (new_individuals[k].chromosomes[0].at(j) == 0)
            {
                int new_var = uni(rng);
                new_individuals[k].chromosomes[0].at(j) = new_var;
            }
        }
    }
}

我这么说只是因为这是我自己最不了解的部分，我可以想象我在那里犯了一个“看不见的”错误。

无论如何，任何帮助将不胜感激。

4

1 回答 1

0

好吧，这只是让您的代码更好、更高效的一种方法。您在std::uniform_int_distribution没有播种的情况下使用，并且连续调用了近 5 次，也许这就是 y 的原因our random number is not really random after all。

一种简单的方法to get things better是seeding the random engine with time，从长远来看，可以提供更好的随机数创建（10000 个人，有点大！）。

这是一个更好的解释的链接，一个简单的测试代码片段如下：

#include <iostream>
#include <random> 

std::default_random_engine generator((unsigned int)time(0));
int random(int n) {
  std::uniform_int_distribution<int> distribution(0, n);
  return distribution(generator);
}
int main() {
        for(int i = 0; i < 15; ++i)
                std::cout << random(5) << " " << random(5)<< std::endl;
        return 0;
}

希望这会有所帮助！干杯，

于 2016-05-15T11:39:16.243 回答