Encog.ML.Genetic.Population.BasicPopulation.Sort C# (CSharp) Method

        public void Sort()
        {
            _genomes.Sort();
        }

 public NeuralGeneticAlgorithm(BasicNetwork network, IRandomizer randomizer, ICalculateScore calculateScore, int populationSize, double mutationPercent, double percentToMate)
     : base(TrainingImplementationType.Iterative)
 {
     NeuralGeneticAlgorithmHelper helper;
     Label_012F:
     helper = new NeuralGeneticAlgorithmHelper();
     helper.CalculateScore = new GeneticScoreAdapter(calculateScore);
     this.Genetic = helper;
     IPopulation population = new BasicPopulation(populationSize);
     this.Genetic.MutationPercent = mutationPercent;
     this.Genetic.MatingPopulation = percentToMate * 2.0;
     this.Genetic.PercentToMate = percentToMate;
     this.Genetic.Crossover = new Splice(network.Structure.CalculateSize() / 3);
     this.Genetic.Mutate = new MutatePerturb(4.0);
     this.Genetic.Population = population;
     int num = 0;
     while (true)
     {
         NeuralGenome genome2;
         if (num < population.PopulationSize)
         {
             BasicNetwork network2 = (BasicNetwork) network.Clone();
             randomizer.Randomize(network2);
             if ((((uint) percentToMate) + ((uint) populationSize)) >= 0)
             {
                 genome2 = new NeuralGenome(network2) {
                     GA = this.Genetic
                 };
             }
         }
         else
         {
             population.Sort();
             if (((uint) populationSize) <= uint.MaxValue)
             {
                 return;
             }
             goto Label_012F;
         }
         if ((((uint) num) + ((uint) populationSize)) >= 0)
         {
             NeuralGenome g = genome2;
             this.Genetic.PerformCalculateScore(g);
             if (((uint) populationSize) > uint.MaxValue)
             {
                 goto Label_012F;
             }
             this.Genetic.Population.Add(g);
             num++;
         }
     }
 }