我需要一个遗传算法（http://www.obitko.com/tutorials/genetic-algorithms/selection.php）code的排名评选方法。 我已创建roullete和锦标赛的选择方法，但现在我需要的排名，我被卡住。 我roullete code是在这里（我用原子结构的遗传原子）：

  const int的轮盘（常量原子* F）
{
  INT I;
  双和，sumrnd;

  总和= 0;
  对于（I = 0; I＆所述N;我+ +）
    总和+ = F（I）.fitness + OFFSET;

  sumrnd = RND（）*总和;

  总和= 0;
  对于（I = 0; I＆所述N;我++）{
    总和+ = F（I）.fitness + OFFSET;
    如果（总和＆GT; sumrnd）
      打破;
  }

  返回我;
}
 

其中原子：

  typedef结构原子
{
  INT格诺[VARS]
  双苯氧[VARS]
  双健身;
} 原子;
 

解决方案

等级的选择是很容易实现的时候，你已经知道了在轮盘赌选择。而不是使用健身为一体的概率为入门选择您使用的等级。因此，对于N方案群的最佳解决方案获得等级N，第二个最好的等级N-1等的最差个体的秩1.现在使用的轮盘赌，并开始选择。

有最佳个体被选择的概率为N /（（N *（N + 1））/ 2）或大约为2 / N为最坏的个体是2 /（N *（N + 1） ），或大约2 / N ^ 2。

这就是所谓的线性秩的选择，因为队伍中形成线性的进展。你也可以把队伍形成了几何级数，如如1 / ^ n，其中n是从1为最佳个体到N最坏的打算的。这当然给的概率要高得多，以最佳的个性化。

您可以看一下在的 HeuristicLab 。

I need code for the ranking selection method on a genetic algorithm (http://www.obitko.com/tutorials/genetic-algorithms/selection.php). I have create roullete and tournament selections method but now I need ranking and I am stuck. My roullete code is here (I am using atom struct for genetic atoms) :

const int roulette (const atom *f)
{
  int i;
  double sum, sumrnd;

  sum = 0;
  for (i = 0; i < N; i++)
    sum += f[i].fitness + OFFSET;

  sumrnd = rnd () * sum;

  sum = 0;
  for (i = 0; i < N; i++) {
    sum += f[i].fitness + OFFSET;
    if (sum > sumrnd)
      break;
  }

  return i;
}

Where atom :

typedef struct atom
{
  int geno[VARS];
  double pheno[VARS];
  double fitness;
} atom;

Rank selection is easy to implement when you already know on roulette wheel selection. Instead of using the fitness as probability for getting selected you use the rank. So for a population of N solutions the best solution gets rank N, the second best rank N-1, etc. The worst individual has rank 1. Now use the roulette wheel and start selecting.

The probability for the best individual to be selected is N/( (N * (N+1))/2 ) or roughly 2 / N, for the worst individual it is 2 / (N*(N+1)) or roughly 2 / N^2.

This is called linear rank selection, because the ranks form a linear progression. You can also think of ranks forming a geometric progression, such as e.g 1 / 2^n where n is ranging from 1 for the best individual to N for the worst. This of course gives much higher probability to the best individual.

You can look at the implementation of some selection methods in HeuristicLab.