C# Population.Sort方法代码示例

        /// <summary>
        /// Returns a solution found using best-placement.
        /// </summary>
        /// <returns></returns>
        protected override IRoute DoSolve(OsmSharp.Tools.Math.TSP.Problems.IProblem problem)
        {
            // create the settings.
            SolverSettings settings = new SolverSettings(
                -1,
                -1,
                1000000000,
                -1,
                -1,
                -1);

            Solver<List<int>, GeneticProblem, Fitness> solver =
                new Solver<List<int>, GeneticProblem, Fitness>(
                new GeneticProblem(problem),
                settings,
                null,
                null,
                null,
                _generation_operation,
                new FitnessCalculator(),
                true, false);

            Population<List<int>, GeneticProblem, Fitness> population =
                new Population<List<int>, GeneticProblem, Fitness>(true);
            while (population.Count < _population_size)
            {
                // generate new.
                Individual<List<int>, GeneticProblem, Fitness> new_individual =
                    _generation_operation.Generate(solver);

                // add to population.
                population.Add(new_individual);
            }

            // select each individual once.
            Population<List<int>, GeneticProblem, Fitness> new_population =
                new Population<List<int>, GeneticProblem, Fitness>(true);
            Individual<List<int>, GeneticProblem, Fitness> best = null;
            int stagnation = 0;
            while (stagnation < _stagnation)
            {
                while (new_population.Count < _population_size)
                {
                    // select an individual and the next one.
                    int idx = OsmSharp.Tools.Math.Random.StaticRandomGenerator.Get().Generate(population.Count);
                    Individual<List<int>, GeneticProblem, Fitness> individual1 = population[idx];
                    Individual<List<int>, GeneticProblem, Fitness> individual2 = null;
                    if (idx == population.Count - 1)
                    {
                        individual2 = population[0];
                    }
                    else
                    {
                        individual2 = population[idx + 1];
                    }
                    population.RemoveAt(idx);

                    Individual<List<int>, GeneticProblem, Fitness> new_individual = _cross_over_operation.CrossOver(solver,
                        individual1, individual2);

                    new_individual.CalculateFitness(solver.Problem, solver.FitnessCalculator);
                    if (new_individual.Fitness.CompareTo(
                        individual1.Fitness) < 0)
                    {
                        new_population.Add(new_individual);
                    }
                    else
                    {
                        new_population.Add(individual1);
                    }
                }

                population = new_population;
                population.Sort(solver, solver.FitnessCalculator);

                new_population = new Population<List<int>, GeneticProblem, Fitness>(true);

                if (best == null ||
                    best.Fitness.CompareTo(population[0].Fitness) > 0)
                {
                    stagnation = 0;
                    best = population[0];
                }
                else
                {
                    stagnation++;
                }

                //// report progress.
                //OsmSharp.Tools.Output.OutputStreamHost.ReportProgress(stagnation,_stagnation,
                //    "OsmSharp.Tools.Math.TSP.EdgeAssemblyGenetic.EdgeAssemblyCrossOverSolver",
                //    "Solving using EAX...");
            }

            List<int> result = new List<int>(best.Genomes);
            result.Insert(0, 0);
//.........这里部分代码省略.........

        // MAIN FUNCTION
        public static bool Run( RandomGeneSig getRandomGeneFunc, FitnessSig getPopFitnessFunc )
        {
            // Runs the EA algorythm, returns true if goal is achieved, false if time is exceeded

            // local variables
            bool goalAchieved;
            double bestFitness, avrFitness;
            double[] popFitness = new double[G.popSize];
            double[][] genes;

            // Create a random population of creatures
            myPop = new Population( G.popSize, G.geneLen, getRandomGeneFunc );

            // MAIN LOOP
            while( true ) {

                // Evaluate and sort population
                genes = myPop.GetGenes();
                myPop.GetFitness( ref popFitness );
                goalAchieved = getPopFitnessFunc( genes, ref popFitness, out bestFitness, out avrFitness );
                myPop.SetFitness( popFitness, bestFitness, avrFitness );
                myPop.Sort();
                //myPop.Print( "EVALUATED", false );
                //Console.ReadKey( true );

                // Are we done?
                if( goalAchieved ) break;

                // Mate Selection
                myPop.SelectToMate( showStepsFlag: true );
                myPop.MatePairing();

                // Create New Creatures
                myPop.CreateOffspring();
                myPop.Mutate();

                // Death Selection
                myPop.SelectToDie( showStepsFlag: false );

                // Pause or continue
                PausePrintContinue();

                // Update population (genes)
                myPop.InsertNewOffspring();
                myPop.IncGeneration();
            }

            // success
            myPop.Print( "(SUCCESS)", false );
            FFNN answer = new FFNN( G.netDef, myPop.creatures[0].Gene);
            Console.WriteLine( answer.ToString() );

            return goalAchieved;
        }

 public void CalculateMedian(Population population)
 {
     population.Sort();
     int middleIndex = population.Size/2;
     population.MedianFitness = population[middleIndex].Fitness;
 }