public void NewGeneration()
        {
            if (!Population.Any())
            {
                return;
            }

            CalculateFitness();

            var newPopulation = new List <Dna <T> >();

            for (int i = 0; i < Population.Count; i++)
            {
                Dna <T> parent1 = ChooseParent();
                Dna <T> parent2 = ChooseParent();

                Dna <T> child = parent1.Crossover(parent2);

                child.Mutate(MutationRate);

                newPopulation.Add(child);
            }

            Population = newPopulation;

            Generation++;
        }
Exemplo n.º 2
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        public Dna <T> Crossover(Dna <T> otherParent)
        {
            var child = new Dna <T>(Genes.Length, _getRandomGene, _fitnessFunction, false);

            for (int i = 0; i < Genes.Length; i++)
            {
                child.Genes[i] = rnd.NextDouble() < 0.5 ? Genes[i] : otherParent.Genes[i];
            }

            return(child);
        }
        private void CalculateFitness()
        {
            _fitnessSum = 0;
            Dna <T> best = Population[0];

            Population.ForEach(p =>
            {
                _fitnessSum += p.CalculateFitness(Population.IndexOf(p));
                best         = best.Fitness < p.Fitness ? p : best;
            });

            BestFitness = best.Fitness;
            best.Genes.CopyTo(BestGenes, 0);
        }