public SelectedSet select(Population population) {
   int N = population.getN();
   int picked, maxPos;
   double maxFit, currentFit;
   SelectedSet selectedSet = new SelectedSet(NS); // Initialize an empty population of size NS.
   for (int i = 0; i < NS; i++) {
     picked = PORTFOLIO.random.nextInt(N); // Random int between 0 and N-1.
     maxFit = population.getFitness(picked);
     maxPos = picked;
     for (int j = 1; j < tourSize; j++) {
       picked = PORTFOLIO.random.nextInt(N);
       currentFit = population.getFitness(picked);
       if (currentFit > maxFit) {
         maxFit = currentFit;
         maxPos = picked;
       }
     }
     selectedSet.setIndividual(i, population.individuals[maxPos], maxFit);
   }
   // selectedSet.computeFitnessStatistics();
   selectedSet
       .computeUnivariateFrequencies(); // NOTE!! Class Subset is responsible for computing all
                                        // frequencies!
   return selectedSet;
 } // END: select(...)
 public SelectedSet select(Population population) {
   int N = population.getN();
   SelectedSet selectedSet = new SelectedSet(NS); // Initialize an empty population.
   int k = N / tourSize; // Number of tournaments within each shuffle.
   int ks = NS / k; // Number of shuffles with exactly 'k' tournaments.
   int rs = NS % k; // Number of tournaments within the last shuffle.
   int ls = ks * k; // Order of the first selection in the last shuffle. We have NS = ls + rs.
   int maxPos = 0;
   for (int i = 0; i < ks; i++) {
     int pos = i * k; // Position of the next selected individual.
     int[] numbers = shuffle(N);
     for (int j = 0; j < k * tourSize; j += tourSize) {
       maxPos = tourSelect(population, selectedSet, numbers, j);
       selectedSet.setIndividual(
           pos++, population.individuals[maxPos], population.getFitness(maxPos));
     }
   }
   int[] numbers = shuffle(N);
   for (int j = 0; j < rs * tourSize; j += tourSize) {
     maxPos = tourSelect(population, selectedSet, numbers, j);
     selectedSet.setIndividual(
         ls++, population.individuals[maxPos], population.getFitness(maxPos));
   }
   // selectedSet.computeFitnessStatistics();
   selectedSet.computeUnivariateFrequencies(); // NOTE!! This is not necessary for SGA!
   return selectedSet;
 } // END: select(..).
 public SelectedSet select(Population population) {
   int N = population.getN();
   sortedPopulation = new PosFit[N];
   for (int i = 0; i < N; i++) sortedPopulation[i] = new PosFit(i, population.getFitness(i));
   Arrays.sort(sortedPopulation); // Sort population in ascending order of fitness.
   SelectedSet selectedSet = new SelectedSet(NS);
   for (int i = 0; i < NS; i++) {
     int best = i + N - NS;
     int position = sortedPopulation[best].getPosition();
     double fitness = sortedPopulation[best].getFitness();
     selectedSet.setIndividual(i, population.individuals[position], fitness);
   }
   // selectedSet.computeFitnessStatistics();
   selectedSet
       .computeUnivariateFrequencies(); // NOTE!! Class Subset is responsible for computing all
                                        // frequencies!
   return selectedSet;
 }