Beispiel #1
0
        private object InnerGet(Type expectType, bool autoResolving, params object[] lastMappingValues)
        {
            if (expectType == null)
            {
                throw new ArgumentNullException(nameof(expectType));
            }

            using (GA.Lock(this.UUID + expectType.AssemblyQualifiedName))
            {
                var callSite = GetCallSite(this, expectType);
                if (callSite == null && autoResolving)
                {
                    callSite = this.AutoResolving(expectType, null);
                    if (callSite == null)
                    {
                        return(null);
                    }
                    CacheType.TryAdd(expectType, new List <ICallSite>(1)
                    {
                        callSite
                    });
                }
                return(callSite?.Invoke(lastMappingValues));
            }
        }
Beispiel #2
0
        public void Remove(Type expectType, string name, bool promote = false)
        {
            if (expectType == null)
            {
                throw new ArgumentNullException(nameof(expectType));
            }
            if (string.IsNullOrWhiteSpace(name))
            {
                throw new ArgumentNullException(nameof(name));
            }
            ConcurrentDictionary <string, ICallSite> dict;

            using (GA.Lock(expectType))
            {
                if (CacheTypeName.TryGetValue(expectType, out dict))
                {
                    ICallSite callSite;
                    dict.TryRemove(name, out callSite);
                }
            }
            if (this._hasParent && promote)
            {
                this.Parent.Remove(expectType, name, promote);
            }
        }
Beispiel #3
0
        private static void SumTo50()
        {
            Console.WriteLine("Sum to 50:...");
            GA ga = new GA(
                20, // Number of individuals
                3, // Number of genes in the genotype
                individual => // Fitness function
                    Math.Abs(50 - individual.Genotype.Sum(val => val))
                );

            ga.RunEpoch(
                500, // Number of generations to run for
                null, // Action to perform for each generation
                () => // Action to perform once fitness has improved
                {
                    Console.WriteLine(
                        "Gen {2}: Fit={1}, Genotype={0}",
                        string.Join(
                            " ",
                            ga.BestIndividual.Genotype.Select(val => val.ToString("0.00"))),
                        ga.BestIndividual.Fitness.ToString("0.00"),
                        ga.CurrentEpochGeneration);
                });

            Console.WriteLine("Sum to 5: done!");
            Console.WriteLine("");
        }
Beispiel #4
0
        private static void Find12345()
        {
            Console.WriteLine("Find 1 2 3 4 5:...");
            GA ga = new GA(
                200, // Number of individuals
                5, // Number of genes in the genotype
                individual => // Fitness function
                Math.Abs(individual.Genotype[0] - 1) +
                Math.Abs(individual.Genotype[1] - 2) +
                Math.Abs(individual.Genotype[2] - 3) +
                Math.Abs(individual.Genotype[3] - 4) +
                Math.Abs(individual.Genotype[4] - 5));

            ga.RunEpoch(500, null, () =>
                {
                    Console.WriteLine(
                        "Gen {2}: Fit={1}, Genotype={0}",
                        string.Join(
                        " ",
                        ga.BestIndividual.Genotype.Select(
                            val => val.ToString("0.00"))),
                        ga.BestIndividual.Fitness.ToString("0.00"),
                        ga.CurrentEpochGeneration);
                });

            Console.WriteLine("Find 1 2 3 4 5: done!");
            Console.WriteLine("");
        }
Beispiel #5
0
 static void CurrentDomain_UnhandledException(object sender, UnhandledExceptionEventArgs e)
 {
     if (e.ExceptionObject is Exception)
     {
         GA.OnGlobalError(sender, e.ExceptionObject as Exception);
     }
     else
     {
         WriteUnhandledException("应用崩溃了:{0}", e.ExceptionObject);
     }
 }
Beispiel #6
0
 static void CurrentDomain_UnhandledException(object sender, UnhandledExceptionEventArgs e)
 {
     //System.Runtime.CompilerServices.RuntimeWrappedException
     if (e.ExceptionObject is Exception)
     {
         GA.OnGlobalError(sender, e.ExceptionObject as Exception);
     }
     else
     {
         WriteUnhandledException("应用崩溃了:{0}", e.ExceptionObject);
     }
 }
        public void FindProjectionMatrix()
        {
            List<WorldToScreenCase> cases =
                new List<WorldToScreenCase>
                {
                    new WorldToScreenCase(688, 349, 0,0 ,0), // 1
                    new WorldToScreenCase(454, 155, 52.5,0,0), // 2
                    new WorldToScreenCase(353, 70,  105,0,0), // 3
                    new WorldToScreenCase(127, 169, 52.5, 34,0), // 4
                    new WorldToScreenCase(155, 411, 0, 34,0), // 5
                    new WorldToScreenCase(220, 364, 0, 34 - 7.32*0.5, 2.44), // 6
                    new WorldToScreenCase(80,  378, 0, 34+7.32*0.5,2.44), // 7
                    new WorldToScreenCase(92,  57,  105, 34 + 7.32*0.5, 2.44), // 8
                    new WorldToScreenCase(145, 56,  105, 34-7.32*0.5,2.44), // 9
                };

            GA ga = new GA(
                5000, // Number of individuals
                16, // Number of genes in the genotype
                individual => // Fitness function
                {
                    double errorSum = 0;
                    foreach (WorldToScreenCase test in cases)
                    {
                        Point3D testScreen = ProjectCase(test.World, individual.Genotype);
                        double sqrError = (testScreen - test.Screen).LengthSquared;
                        errorSum += sqrError;
                    }

                    return errorSum;
                });

            ga.RunEpoch(100000,
                () =>
                {
                    ga.BreakEpochRun = ga.BestIndividual.Fitness <= 1.0 || stopButton.Enabled == false;
                    Application.DoEvents();
                },
                () =>
                {
                    generationLabel.Text = string.Format(
                        "Gen {2}: Fit={1}, Genotype={0}",
                        string.Join(
                        " ",
                        ga.BestIndividual.Genotype.Select(val => val.ToString("0.00"))),
                        ga.BestIndividual.Fitness.ToString("0.00"),
                        ga.CurrentEpochGeneration);
                    DrawCasesToBitmap(cases, ga.BestIndividual.Genotype);
                });
        }
Beispiel #8
0
        static void Main(string[] args)
        {
            GA Ga;
            TravellingSalesman1dObjective Objective;
            Galib.Mutation.IntegerSwapMutator Mutator;
            IntegerChromosomeSpecifier ChromosomeSpecifier;
            
            //
            // Create the objective.
            //
            int[] cities = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
            Objective = new TravellingSalesman1dObjective(cities);
            
            //
            // Create the mutator
            //
            Mutator = new Galib.Mutation.IntegerSwapMutator();

            //
            // Create the chromosome specifier.
            //
            ChromosomeSpecifier = new IntegerChromosomeSpecifier();
            ChromosomeSpecifier.Length = numberOfCities;
            ChromosomeSpecifier.MinValue = 0;
            ChromosomeSpecifier.MaxValue = numberOfCities;
            ChromosomeSpecifier.PositionDependent = true;

            //
            // Create the recombinator
            //
            Galib.Recombination.PartiallyMatchedRecombinator Recombinator;
            Recombinator = new Galib.Recombination.PartiallyMatchedRecombinator();

            Ga = new GA();

            Ga.PopulationSize       = 100;
            Ga.Mutator              = Mutator;
            Ga.ChromosomeSpecifier  = ChromosomeSpecifier;
            Ga.Objective            = Objective;
            Ga.InvertedObjective    = true;
            Ga.Recombinator         = Recombinator;
            Ga.MaxGenerations       = 500;

            Ga.Run();

            Console.WriteLine("Best Individual:");
            Console.WriteLine( Ga.Population.BestChromosome );
            Console.WriteLine("Finished.  Hit return.");
            Console.ReadLine();
        }
Beispiel #9
0
        /// <summary>
        /// 如果结果出现异常则抛出错误,否则不做任何处理。
        /// </summary>
        /// <typeparam name="TResult">结果的数据类型。</typeparam>
        /// <param name="result"><see cref="System.Result"/> 的派生类实例。</param>
        public static TResult ThrowIfFailded <TResult>(this TResult result)
            where TResult : Result
        {
            if (result == null)
            {
                throw new ArgumentNullException("result");
            }

            if (result.IsFailed)
            {
                GA.OnGlobalError(result, result.Exception);
                throw result.Exception;
            }
            return(result);
        }
Beispiel #10
0
        private static void OnceInvoke(object state)
        {
            AsyncJob tme = state as AsyncJob;

            if (tme.WaitForNextTask())
            {
                return;
            }
            try
            {
                tme.RunTask();
            }
            catch (Exception ex)
            {
                GA.OnGlobalError(tme, ex);
                throw;
            }
        }
Beispiel #11
0
        /// <summary>
        /// 写入未捕获的异常。该异常不记录到日志管理器,而是独立出一个 LogError{yyyy-MM-dd}.txt 文件。
        /// </summary>
        /// <param name="message">复合格式的错误消息。</param>
        /// <param name="args">一个对象数组,其中包含零个或多个要设置格式的对象。</param>
        public static void WriteUnhandledException(string message, params object[] args)
        {
            var path = GA.IsWebRuntime ? System.Web.Webx.MapUrl("~/LogError" + DateTime.Now.ToString("yyyy-MM-dd") + ".txt") : GA.FullPath("LogError" + DateTime.Now.ToString("yyyy-MM-dd") + ".txt");

            System.IO.File.AppendAllText(path, DateTime.Now + " " + string.Format(message, args));
        }
Beispiel #12
0
 private void ShowTestResult(GA ga, WorldToScreenCase test)
 {
     Point3D p = ProjectCase(test.World, ga.BestIndividual.Genotype);
 }
 public static extern HWND GetAncestor(HWND hwnd, GA gaFlags);
Beispiel #14
0
 public static extern IntPtr GetAncestor(IntPtr hWnd, GA gaFlags);
Beispiel #15
0
 /// <summary>
 /// 创建一个模拟对象。
 /// </summary>
 /// <typeparam name="TModel">对象的数据类型。</typeparam>
 /// <returns>要一个模拟的对象。</returns>
 protected virtual TModel CreateMock <TModel>() => GA.CreateMockModel <TModel>();
        private void bw_DoWork(object sender, DoWorkEventArgs e)
        {
            BackgroundWorker worker = sender as BackgroundWorker;
            GaTask task = (GaTask)e.Argument;
            
            try
            {
                task.ProgressPercent = 0;
                task.State = TaskState.Launched;
                task.LaunchTime = DateTime.Now;

                IList<IIndividual<Wrapper<LearningObject>>> individualsList = new List<IIndividual<Wrapper<LearningObject>>>();

                //Individuals initializing
                for (int i = 0; i < task.GaConfigs.PopulationSize; i++)
                {
                    //Wrap source LOs to pass to GA
                    //List<Wrapper<LearningObject>> wrappedListOfLo = new List<Wrapper<LearningObject>>(); = task.SourceLearningObjects.Select(sourceLearningObject => new Wrapper<LearningObject>(GAUtils.GetProbability(0.5), sourceLearningObject)).ToList();

                    List<Wrapper<LearningObject>> wrappedListOfLo = new List<Wrapper<LearningObject>>();
                    foreach (var sourceLearningObject in task.SourceLearningObjects)
                    {
                        wrappedListOfLo.Add(new Wrapper<LearningObject>(GAUtils.GetProbability(0.5), sourceLearningObject));
                    }

                    wrappedListOfLo = GAUtils.Shuffle(wrappedListOfLo).ToList();
                    individualsList.Add(new Individual<Wrapper<LearningObject>>(wrappedListOfLo));
                }

                var population = new Population<Wrapper<LearningObject>>(individualsList, IndividualEvaluateFunc, MutationFunc, CrossoverFunc, SelectionFunc);
                IGeneticAlgorithm<Wrapper<LearningObject>> ga = new GA<Wrapper<LearningObject>>(population);
                IGAconfiguration configuration = new GAConfiguration(task.GaConfigs.GAIterationsNumber, 
                    task.GaConfigs.MutationProbability, task.GaConfigs.MutationPeriod, task.GaConfigs.CrossoverProbability, 
                    task.GaConfigs.ElitismPercentage);

                ga.Run(StopCondition, configuration, task.Id, worker);
                if (worker != null && worker.CancellationPending)
                {
                        task.State = TaskState.Cancelled;
                        e.Result = task;
                }
                else
                {
                    task.State = TaskState.Finished;
                }
                  
                if (task.State == TaskState.Finished)
                {
                    if (worker != null) worker.ReportProgress(100, task.Id);
                }

                //Put result data to task
                task.AverageFFvalues = ga.Statistics.AverageFitnessFunctionValues;
                task.BestFfValues = ga.Statistics.BestFitnessFunctionValues;
                task.WorstFfValues = ga.Statistics.WorstFitnessFunctionValues;
                List<LearningObject> resultList = new List<LearningObject>();
                foreach (var wrapper in ga.BestIndividual.Chromosome.Genes)
                {
                    if (wrapper.Used)
                    {
                        resultList.Add(wrapper.Value);
                    }
                }
                task.ResultLearningObjects = resultList;
                e.Result = task;
            }
            catch (Exception ex)
            {
                Console.WriteLine(ex.Message);

                task.State = TaskState.Error;
                task.ErrorMessage = ex.Message;
                e.Result = task;
            }
        
        }
Beispiel #17
0
        public static void FindProjectionMatrix()
        {
            List<WorldToScreenCase> cases =
                new List<WorldToScreenCase>
                {
                    // New image
                    /*new WorldToScreenCase(690, 350,       0,      0),
                    new WorldToScreenCase(455, 155, 105*0.5,      0),
                    new WorldToScreenCase(353,  70,     105,      0),
                    new WorldToScreenCase(128, 169, 105*0.5, 68*0.5),
                    new WorldToScreenCase(153, 412,       0, 68*0.5),*/

                    // Old Image
                    new WorldToScreenCase(165,178 ,       0,      0),
                    new WorldToScreenCase(377,120 ,       52.5,      0),
                    new WorldToScreenCase(513,85 ,       105,      0),
                    new WorldToScreenCase(473,157 ,       52.5,      34),
                    new WorldToScreenCase(611,208 ,       52.5,      68),
                    new WorldToScreenCase(735,136 ,       105,      68),
                };

            GA ga = new GA(
                2000, // Number of individuals
                16, // Number of genes in the genotype
                individual => // Fitness function
                {
                    double errorSum = 0;
                    foreach (WorldToScreenCase test in cases)
                    {
                        Point3D testScreen = ProjectCase(test.World, individual.Genotype);
                        double sqrError = (testScreen - test.Screen).LengthSquared;
                        errorSum += sqrError;
                    }

                    return errorSum;
                });

            ga.RunEpoch(10000,
                () =>
                {
                    ga.BreakEpochRun = ga.BestIndividual.Fitness <= 1.0 || Console.KeyAvailable;
                },
                () =>
                {
                    Console.WriteLine(
                        "Gen {2}: Fit={1}, Genotype={0}",
                        string.Join(
                        " ",
                        ga.BestIndividual.Genotype.Take(5).Select(
                            val => val.ToString("0.00"))),
                        ga.BestIndividual.Fitness.ToString("0.00"),
                        ga.CurrentEpochGeneration);
                });

            if (Console.KeyAvailable)
            {
                Console.ReadKey();
            }

            Console.WriteLine("Results for training set:");
            foreach (WorldToScreenCase test in cases)
            {
                ShowTestResult(ga, test);
            }

            Console.WriteLine("");
            Console.WriteLine("Additional tests:");
            ShowTestResult(ga, new WorldToScreenCase(120, 73, 105, 34));

            Console.WriteLine("Find ProjectionMatrix: done!");
            Console.WriteLine("");
        }
Beispiel #18
0
 private static void ShowTestResult(GA ga, WorldToScreenCase test)
 {
     Point3D p = ProjectCase(test.World, ga.BestIndividual.Genotype);
     Console.WriteLine(
         " World ({0:0.00}, {1:0.00}) => \n" +
         "   Wanted:({2:0.00}, {3:0.00}) \n" +
         "   Received:({4:0.00}, {5:0.00})",
         test.World.X,
         test.World.Y,
         test.Screen.X,
         test.Screen.Y,
         p.X,
         p.Y);
 }
Beispiel #19
0
        private void Hoskin(int n, double L, double H, bool cum, double startPoint, double volatiles)
        {
            int m = n;// (int)Math.Pow(n, 2);
            double[] phi=new double[m];
            double[] cov = new double[m];
            double[] psi = new double[m];
            double[] output = new double[m];
            double[] output2 = new double[m];
            while (true)
            {
            double v = 1;
            output[0]=snorm();
            phi[0]=0;
            for (int i = 0; i < m; i++)
                cov[i]=covariance(i, H);

            /* simulation */
            for (int i = 1; i < m; i++)
            {
                phi[i - 1] = cov[i];
                for (int j = 0; j < i - 1; j++)
                {
                    psi[j] = phi[j];
                    phi[i - 1] -= psi[j] * cov[i - j - 1];
                }
                phi[i - 1] /= v;
                for (int j = 0; j < i - 1; j++)
                {
                    phi[j] = psi[j] - phi[i - 1] * psi[i - j - 2];
                }
                v *= (1 - phi[i - 1] * phi[i - 1]);

                output[i] = 0;
                for (int j = 0; j < i; j++)
                {
                    output[i] += phi[j] * output[i - j - 1];
                }
                output[i] += Math.Sqrt(v) * snorm();
            }

            /* rescale to obtain a sample of size 2^(*n) on [0,L] */
            double scaling = Math.Pow(L / m, H);
            for (int i = 0; i < m; i++)
            {
                output2[i] = scaling * (output[i]);
                if (cum && i > 0)
                {
                    output2[i] += output[i - 1];
                }
            }

            //Start the stopwatch
            Stopwatch s = new Stopwatch();
            s.Start();

            //Set up the Model
            List<double>outputv=new List<double>();
            List<double>outputv2=new List<double>();
            for (int i = 0; i < m; i++)
            {
                outputv.Add(output[i]);
                outputv2.Add(output2[i]);
            }

            Model model = new Model(startPoint, H, outputv, outputv2, startPoint + 0.01000, m, volatiles);

            //Set up the GA and run it
            Int32 threads = 10;
            GA ga = new GA(model,threads, 0.8, 0.0005, 1000);

                ga.Go();
                //===============================================================================================
                //In this example we have set the model, progress reporter delegate (which is optional), number
                //of threads, crossover rate, mutation rate, population size, number of generations
                //===============================================================================================

                //Get the results
                s.Stop();
                Console.WriteLine();
                Console.WriteLine("Optimisation run finished in " + s.ElapsedMilliseconds.ToString() + " ms.");
                double[] bestGenes; double bestFitness;
                ga.GetBest(out bestGenes, out bestFitness);
                if (bestFitness > 0.1)
                {
                    double old_price = startPoint;//clo->stock_price;
                    double old_volatilities = volatiles; //bestGenes[0];//clo->variance;
                    double mean_reversion_rate = bestGenes[0] / 10000000;
                    double mean_reversion_level = bestGenes[1] / 1000000;
                    double dt = bestGenes[2] / 10000000;
                    double variance_variance = bestGenes[3] / 10000000;
                    double risk_free_rate = bestGenes[4] / 10000000;

                    for (int i = 0; i < n; i++)
                    {
                        double new_volatilities = old_volatilities +
                               mean_reversion_rate * (mean_reversion_level - old_volatilities) * dt +
                               variance_variance * output[i];

                        double new_price = old_price +
                               risk_free_rate * old_price * dt +
                               new_volatilities * old_price * output2[i]; //These are already scaled via hoskin */
                        Ticks.Add(new_price);
                        old_price = new_price;
                        old_volatilities = new_volatilities;
                    }

                    break;
                }
            }
              /**/

            /*  for (int i = 0; i < n; i++)
              {
                /*  double new_volatilities = old_volatilities +
                               mean_reversion_rate * (mean_reversion_level - old_volatilities) * dt +
                               variance_variance * output[i];

                  double new_price = old_price +
                              risk_free_rate * old_price * dt +
                              new_volatilities * old_price * output2[i]; //These are already scaled via hoskin */
                  /*double new_volatilities = old_volatilities + variance_variance*output[i];
                  double new_price = old_price + new_volatilities * output2[i];
                  Ticks.Add(new_price);
                  old_price = new_price;
                  old_volatilities = new_volatilities;
              }*/
        }
        static void Main()
        {
            IGAconfiguration configuration = new GAConfiguration(100, 0.9, null, 0.6, 0.3);

            IList<IIndividual<Wrapper<MyValuableClass>>> individualsList = new List<IIndividual<Wrapper<MyValuableClass>>>();

            //Individuals initializing
            for (int i = 0; i < 1000; i++)
            {
                //Wrap source LOs to pass to GA
                List<Wrapper<MyValuableClass>> wrappedList = new List<Wrapper<MyValuableClass>>();
                for (int j = 0; j < 5; j++)
                {
                    wrappedList.Add(new Wrapper<MyValuableClass>(new MyValuableClass(GAUtils.GetRandom(0, 100))));
                }
                individualsList.Add(new Individual<Wrapper<MyValuableClass>>(wrappedList));
            }

            Console.WriteLine("Population creating");
            var population = new Population<Wrapper<MyValuableClass>>(individualsList, IndividualEvaluateFunc, MutationFunc, CrossoverFunc, SelectionFunc);
            IGeneticAlgorithm<Wrapper<MyValuableClass>> ga = new GA<Wrapper<MyValuableClass>>(population);

            ObjectId id = new ObjectId();
            Console.WriteLine("GA launching");
            BackgroundWorker bgBackgroundWorker = new BackgroundWorker();
            ga.Run(StopCondition, configuration, id, bgBackgroundWorker, WriteToCOnsole);

            foreach (var val in ga.Statistics.AverageFitnessFunctionValues)
            {
                Console.WriteLine(val);
            }

            string fileName = "data.txt";
            using (StreamWriter outfile = new StreamWriter(fileName))
            {
                foreach (var averageFitnessFunctionValue in ga.Statistics.AverageFitnessFunctionValues)
                {
                    outfile.WriteLine(averageFitnessFunctionValue);
                }
            }
            //ga.Run(statistics => statistics.CurrentIteration > configuration.IterationsNumber, configuration, id, bgBackgroundWorker, WriteToCOnsole);
        }
Beispiel #21
0
        private void Optimize()
        {
            SortedList<int, int> Result = new SortedList<int, int>();
            lblScanCount.Text = "ScanCount:---";
            lblScanMaxCount.Text = "ScanMaxCount:---";

            for (nudAWin.Value = 500; nudAWin.Value > 0; nudAWin.Value--)
            {

                Simulate(gPairList,20, 30, 100, 0, (double)nudAWin.Value);

                int AWCount = 0;
                int ScanCount = 0;
                int ScanMaxCount = 0;
                Analyze(gPairList,out AWCount, out ScanCount, out ScanMaxCount);
                //lblAWC.Text = "AWCount: " + AWCount.ToString();

                Result.Add((int)nudAWin.Value, AWCount);
            }

            KeyValuePair<int, int> Best = Result.First(x => (x.Value == Result.Values.Min()));
            nudAWin.Value = Best.Key;
            lblAWC.Text = "AWCount: " + Best.Value.ToString();

            Random rand = new Random();
            GA ga = new GA(500, 3,
            individual =>
            {
                List<Pair> PairList = gPairList.ToList(); ;
                Simulate(PairList,Math.Abs(individual.Genotype[0] * 100),
                    Math.Abs(individual.Genotype[1] * 100),
                    Math.Abs(individual.Genotype[2] * 100),
                    0/* Math.Abs(individual.Genotype[3] * 100)*/,
                    (double)nudAWin.Value);

                int AWCount = 0;
                int ScanCount = 0;
                int ScanMaxCount = 0;
                Analyze(PairList,out AWCount, out ScanCount, out ScanMaxCount);

                return ((Math.Pow(AWCount - ScanMaxCount, 2) / 4) + Math.Pow(AWCount - ScanCount, 2) + Math.Abs(individual.Genotype[2]) * 10 + Math.Abs(individual.Genotype[1]) * 100 + Math.Abs(individual.Genotype[0]) * 100 + (Math.Abs(individual.Genotype[1]) < Math.Abs(individual.Genotype[0]) ? 100 : 0));
            },
            individual =>
            {
                individual.Genotype[0] = 2 * rand.NextDouble();
                individual.Genotype[1] = 2 * rand.NextDouble();
                individual.Genotype[2] = 2 * rand.NextDouble();
            });

            backgroundWorker1.RunWorkerAsync(ga);

            /* nudScanTime.Value=(decimal)Math.Abs(ga.BestIndividual.Genotype[0]*100);
             nudMaskTime.Value = (decimal)Math.Abs(ga.BestIndividual.Genotype[1]*100);
             nudThresold.Value = (decimal)Math.Abs(ga.BestIndividual.Genotype[2]*100);
             nudRet.Value = 0;//(decimal)Math.Abs(ga.BestIndividual.Genotype[3] * 100);
             */

            //double ScanTime = (double);
            //double MaskTime = (double)
            //double Thresold = (double)
        }
Beispiel #22
0
        private void btn_Run_Click(object sender, System.EventArgs e)
        {
            try
            {
                if (Points.Count < 2)
                {
                    MessageBox.Show("No Enough cities");
                    return;
                }
                this.m_chro = null;
                Refresh();
                //�¼ӵIJ���
                points.Clear();
                temps.Clear();
                DrawPoints();
                GALib.Initializer newItializer = new GALib.Initializer(this.Initializer);
                GALib.Mutate	  mutater	   = new GALib.Mutate(this. ChromoseCompraror);
                GALib.Fitness	  fitmethod	   = new GALib.Fitness(this.Fitness);
                GALib.CrossOver	  CrossMethod  = new GALib.CrossOver(this.CrossOver);

                GALib.GA GAAlgo			= new GA(newItializer,fitmethod,mutater,CrossMethod);
                GAAlgo.Generations		= long.Parse(this.num_Gnr.Value.ToString());
                GAAlgo.PopulationSize	= ushort.Parse(this.num_PopSiz.Value.ToString());
                GAAlgo.Mutation			= double.Parse(this.num_Mutation.Value.ToString());
                GAAlgo.CrossOver		= double.Parse(this.numCO.Value.ToString());

                if(Tool_MainBar.Buttons[1].Pushed)
                {
                    GAAlgo.EnableLogging = true;
                    GAAlgo.LogFilePath   = this.openFileDialog.FileName;

                }

                GAAlgo.Initialize();

                while (!GAAlgo.IsDone())
                    GAAlgo.CreateNextGeneration();

                m_chro = GAAlgo.GetBestChromosome();
                DrawCitiesPath();
            }
            catch(System.FormatException exp)
            {
                MessageBox.Show("Please check your Input Parameters "+exp);
            }
        }
Beispiel #23
0
 public GenomePair(string id, GA.Genome gen)
 {
     Id = id;
     Genome = gen;
 }