Ranking Selection in Genetic Algorithm code

Question

I need code for the ranking selection method on a genetic algorithm. I have create roulette and tournament selections method but now I need ranking and I am stuck.

My roulette 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;

Answer 1

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.

Answer 2

My code of Rank Selection in MatLab:

NewFitness=sort(Fitness);
NewPop=round(rand(PopLength,IndLength));

for i=1:PopLength
    for j=1:PopLength
        if(NewFitness(i)==Fitness(j))
            NewPop(i,1:IndLength)=CurrentPop(j,1:IndLength);
            break;
        end
    end
end
CurrentPop=NewPop;

ProbSelection=zeros(PopLength,1);
CumProb=zeros(PopLength,1);

for i=1:PopLength
    ProbSelection(i)=i/PopLength;
    if i==1
        CumProb(i)=ProbSelection(i);
    else
        CumProb(i)=CumProb(i-1)+ProbSelection(i);
    end
end

SelectInd=rand(PopLength,1);

for i=1:PopLength
    flag=0;
    for j=1:PopLength
        if(CumProb(j)<SelectInd(i) && CumProb(j+1)>=SelectInd(i))
            SelectedPop(i,1:IndLength)=CurrentPop(j+1,1:IndLength);
            flag=1;
            break;
        end
    end
    if(flag==0)
        SelectedPop(i,1:IndLength)=CurrentPop(1,1:IndLength);
    end
end

Answer 3

I've made a template genetic-algorithm class in C++.

My library of genetic algorithm is separated from GeneticAlgorithm and GAPopulation. Those are all template classes so that you can see its origin code in API Documents.

• Here are source codes and API documents.
  • http://samchon.github.io/framework/api/cpp/d5/d28/classsamchon_1_1library_1_1GeneticAlgorithm.html
  • http://samchon.github.io/framework/api/cpp/d8/dcd/classsamchon_1_1library_1_1GAPopulation.html