static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
//Application.Run(new Painter());
//setupNext();
var chromosome = new FloatingPointChromosome(
//lvl, hp, food, ruletype, rule, seed, tempo, pitch, r1, r2, r3
new double[] { 0, 0, 0, 7, 30, 32, 23, 23, 0, 0, 0 }, //min values
new double[] { 50, 500, 5, 1800, 999999999, 9999999, 288, 72, 999, 999, 999 }, //max values
new int[] { 6, 9, 3, 11, 34, 27, 9, 7, 10, 10, 10 }, //bits required for values
new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 } //bits for fractals
);
var population = new Population(5, 25, chromosome); //min-max population
var fitness = new myFitness();
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new FlipBitMutation();
var termination = new GenerationNumberTermination(10);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.Start();
}
private GeneticAlgorithm RunGeneticAlgorithm(Population population, IFitness fitness, int generations)
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(generations),
TaskExecutor = new ParallelTaskExecutor()
{
MinThreads = 100, MaxThreads = 250
},
};
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestfitness = ga.BestChromosome.Fitness ?? 0;
if (bestfitness != latestFitness)
{
latestFitness = bestfitness;
testOutputHelper.WriteLine($"Current best fitness: {bestfitness}");
}
};
ga.Start();
return(ga);
}
public void Play2048PatternTest2()
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new PartialShuffleMutation();
var fitness = new ClosestToMillion();
var bestChromosomeTodate = new Play2048Chromosome(20, new List <string>()
{
"up", "down", "left", "right"
});
//bestChromosomeTodate.Pattern = "";
var population = new Population(4, 5, bestChromosomeTodate);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(1);
Console.WriteLine("GA running...");
ga.GenerationRan += Ga_GenerationRan;
ga.Population.BestChromosomeChanged += Population_BestChromosomeChanged;
ga.Init();
//queue up all the patterns...
Console.WriteLine((bestChromosome as Play2048Chromosome).Pattern);
Console.WriteLine("Best solution found has {0} fitness was born in generation {1}", ga.BestChromosome.Fitness, bestGen);
}
public void Cross_LessGenesThenSecondSwapPoint_Exception()
{
var target = new TwoPointCrossover(1, 3);
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(3);
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("parents", "The swap point two index is 3, but there is only 3 genes. The swap should result at least one gene to each sides."), () =>
{
target.Cross(new List <IChromosome>()
{
chromosome1,
chromosome1
});
});
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(4);
ExceptionAssert.IsThrowing(new ArgumentOutOfRangeException("parents", "The swap point two index is 3, but there is only 4 genes. The swap should result at least one gene to each sides."), () =>
{
target.Cross(new List <IChromosome>()
{
chromosome2,
chromosome2
});
});
}
public static void RunExampleOnce()
{
List <Person> population = new List <Person>();
List <double> coefficient = new List <double>();
List <CoupleParent> coupleParents = new List <CoupleParent>();
List <string> crossoverChildren = new List <string>();
FileReader.CoefficientRead(coefficient);
FileReader.ReadFile(population, 5);
Fitness.CalculateSSE(population);
Fitness.CalculateCoefSSE(coefficient, population);
//Stap 1 t/m 5
RouletteWheel.DoCalculation(population);
//Crossover Methods
//SinglePointCrossover.DoCrossover(coupleParents, crossoverChildren);
TwoPointCrossover.DoCrossover(coupleParents, crossoverChildren, crossOverPercentage, population);
//Mutation
Mutation.MutationChildren(crossoverChildren);
//Recalculate Fitness for Childrens
List <Person> childrenPopulation = Fitness.CalculateCoefSSEChild(coefficient, crossoverChildren);
//Elitism best solution
var bestPerson = Elitism.ChildHighestFit(childrenPopulation, crossOverPercentage);
Console.ReadKey();
}
public IChromosome Compute(TrainingModel <ContextInfo> model, IChromosome baseChromosome)
{
model.InitializePartitions(_folds);
var sw = new Stopwatch();
sw.Start();
var selection = new EliteSelection();
var crossover = new TwoPointCrossover(); // Or OnePoint
var mutation = new UniformMutation(true);
var fitness = new Fitness(model);
var chromosome = baseChromosome == null ? new Chromosome() : baseChromosome;
var population = new Population(2, 5, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new OrTermination(
new GenerationNumberTermination(5),
new FitnessStagnationTermination(),
new FitnessThresholdTermination(baseChromosome.Fitness.HasValue ? baseChromosome.Fitness.Value : 0.0))
};
Console.WriteLine("GA running...");
ga.Start();
sw.Stop();
Console.WriteLine("Best solution found has {0} fitness. Time elapsed {1}", ga.BestChromosome.Fitness, sw.Elapsed);
foreach (var gene in ga.BestChromosome.GetGenes())
{
Console.WriteLine(gene);
}
return(ga.BestChromosome);
}
public void Cross_LessGenesThenSecondSwapPoint_Exception()
{
var target = new TwoPointCrossover(1, 3);
var chromosome1 = Substitute.For <ChromosomeBase>(3);
Assert.Catch <ArgumentOutOfRangeException>(() =>
{
target.Cross(new List <IChromosome>()
{
chromosome1,
chromosome1
});
}, "The swap point two index is 3, but there is only 3 genes. The swap should result at least one gene to each sides.");
var chromosome2 = Substitute.For <ChromosomeBase>(4);
Assert.Catch <ArgumentOutOfRangeException>(() =>
{
target.Cross(new List <IChromosome>()
{
chromosome2,
chromosome2
});
}, "The swap point two index is 3, but there is only 4 genes. The swap should result at least one gene to each sides.");
}
public void Play2048PatternTest()
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new PartialShuffleMutation();
var fitness = new ClosestToMillion();
var chromosome = new Play2048Chromosome(20, new List <string>()
{
"up", "down", "left", "right"
});
var population = new Population(22, 42, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(10);
Console.WriteLine("GA running...");
ga.GenerationRan += Ga_GenerationRan;
ga.Population.BestChromosomeChanged += Population_BestChromosomeChanged;
Task.Run(async() => { await Start(ga); }).Wait();
GA.GetSingleton().NextPattern = string.Empty;
Console.WriteLine((bestChromosome as Play2048Chromosome).Pattern);
Console.WriteLine("Best solution found has {0} fitness was born in generation {1}", ga.BestChromosome.Fitness, bestGen);
}
static void Main(string[] args)
{
var get = new DataReader();
//fitness
Fitness fitness = new Fitness(get.DataPregnant(), get.Data());// insert the data from DataReader for the population creation.
//selection
var highestFitness = 1;
var lowestFitness = 2;
ISelection roulette = new RouletteWheel();
ISelection ranking = new Ranking(highestFitness);
//crossover
var crossoverProbability = 0.6;
ICrossover onePointCrosseover = new OnePointCrossover();
ICrossover twoPointCrosseover = new TwoPointCrossover();
//mutation
var Mutation = (chanceMutation : 0.01, min : -1, max : 1);
var RandomLimits = (min : -1, max : 1);
//population
var populationSize = 60;
// create a new population
var population = fitness.GenerateNewPopulation(populationSize, RandomLimits);
var ga = new GeneticAlgorithm(population, fitness, 100, onePointCrosseover, crossoverProbability, Mutation, roulette);
System.Collections.Generic.List <double> predict = get.Data()[400];
Console.WriteLine("prediction for value " + 400 + " : " + new Prediction().Predict(ga.BestSeed.DNA, predict));
Console.ReadLine();
}
public void Run()
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new MyMutation();
var fitness = new MyProblemFitness(pointList.ToArray());
var chromosome = new MyProblemChromosome(pointList.Count);
var population = new Population(100, 100, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessStagnationTermination(100),
MutationProbability = 0.5f, //KS:变异概率50%
CrossoverProbability = 0.5f, //KS:交配概率50%
};
int index = 0; //KS:计算代数
ga.GenerationRan += delegate
{
var bestChromosome = ga.Population.BestChromosome;
Console.Write("Index: " + index);
Console.Write(", Fitness: {0}", bestChromosome.Fitness);
Console.Write(", Genes: {0}", string.Join("-", bestChromosome.GetGenes()));
Console.WriteLine();
index++;
};
Console.WriteLine("GA running...");
Stopwatch SW = new Stopwatch();
SW.Start();
ga.Start();//调用GA线程
SW.Stop();
//Console.Write(", Time: {0}", SW.ElapsedMilliseconds);
//Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
fitness.Evaluate(ga.BestChromosome); //KS: 评价基因的优劣
startPoint = new Point(0, (double)fitness.lr.Alpha);
endPoint = new Point(screenWidth, (double)(fitness.lr.Alpha + screenWidth * fitness.lr.Beta));
}
public void Cross_ChromosomeLengthLowerThan3_Exception()
{
var target = new TwoPointCrossover(0, 1);
var chromosome1 = Substitute.For <ChromosomeBase>(2);
Assert.Catch <CrossoverException>(() =>
{
target.Cross(new List <IChromosome>()
{
chromosome1,
chromosome1
});
}, "A chromosome should have, at least, 3 genes. {0} has only 2 gene.".With(chromosome1.GetType().Name));
}
public static void RunCrossOver(List <CoupleParent> coupleParents, List <string> crossoverChildren, List <Person> population)
{
Random random = new Random();
int setCrossoverPoint = random.Next(0, 1);
if (setCrossoverPoint == 0)
{
SinglePointCrossover.DoCrossover(coupleParents, crossoverChildren, crossOverPercentage, population);
}
else
{
TwoPointCrossover.DoCrossover(coupleParents, crossoverChildren, crossOverPercentage, population);
}
}
public void ItCanPerformACrossover()
{
var config = GATestHelper.GetTravelingSalesmanDefaultConfiguration();
var father = GATestHelper.GetAlphabetCharacterChromosome();
var mother = GATestHelper.GetAlphabetCharacterChromosome();
mother.Genes.Shuffle(config.Random);
var twoPoint = new TwoPointCrossover();
var child = twoPoint.Execute(father, mother, config);
Assert.AreNotEqual(father.ToString(), child.ToString());
Assert.AreNotEqual(mother.ToString(), child.ToString());
}
public void Cross_ChromosomeLengthLowerThan3_Exception()
{
var target = new TwoPointCrossover(0, 1);
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(2);
ExceptionAssert.IsThrowing(new CrossoverException(target, "A chromosome should have, at least, 3 genes. {0} has only 2 gene.".With(chromosome1.GetType().Name)), () =>
{
target.Cross(new List <IChromosome>()
{
chromosome1,
chromosome1
});
});
}
public void TwoPointCrossLinkTest1()
{
Organism parent1 = new Organism();
Organism parent2 = new Organism();
parent1.Chromosomes.Add(new Chromosome(1, "111"));
parent2.Chromosomes.Add(new Chromosome(1, "000"));
IRandom rand = new Deterministic(1, 0, 2);
TwoPointCrossover crossLinker = new TwoPointCrossover(rand, 1);
var answer = crossLinker.CrossLink(parent1, parent2);
Assert.AreEqual("001", answer.Item1.Chromosomes[0].ToString());
Assert.AreEqual("110", answer.Item2.Chromosomes[0].ToString());
}
static void Main(string[] args)
{
//
GraphModel graphModel = new GraphModel("data/data_35ver.csv");
int vezirSayisi = graphModel.vertexes.Count;
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness(graphModel);
var chromosome = new MyProblemChromosome(vezirSayisi);
var population = new Population(5000, 5500, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
//Termination = new FitnessThresholdTermination(vezirSayisi * (vezirSayisi - 1) / 2)
Termination = new FitnessThresholdTermination(100)
};
int index = 0;
ga.GenerationRan += delegate {
var bestChromosome = ga.Population.BestChromosome;
Console.Write("Index: " + index);
Console.Write(", Fitness: {0}", bestChromosome.Fitness);
Console.Write(", Genes: {0}", string.Join("-", bestChromosome.GetGenes()));
bestChromosome.GetGenes();
Console.Write($", Ilość kolorow: {MyProblemFitness.GetCountOfColors(bestChromosome.GetGenes())}");
Console.WriteLine();
index++;
};
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
Console.Read();
}
public void TestTwoPointCrossOver_ItemsIndex1And2SwappedInChildren()
{
var r = new InsertNextItemRandomizer(1, 3);
var tpco = new TwoPointCrossover(r);
var c = tpco.DoCrossover(_moveList4Moves1, _moveList4Moves2);
AssertMove(c.Item1[0], Color.Blue, Color.Red, CoordinateProvider.GetCoordinate('F', 7));
AssertMove(c.Item1[1], Color.White, Color.Cyan, CoordinateProvider.GetCoordinate('E', 7));
AssertMove(c.Item1[2], Color.Red, Color.Blue, CoordinateProvider.GetCoordinate('A', 5));
AssertMove(c.Item1[3], Color.Blue, Color.Red, CoordinateProvider.GetCoordinate('C', 6));
AssertMove(c.Item2[0], Color.Blue, Color.Red, CoordinateProvider.GetCoordinate('F', 2));
AssertMove(c.Item2[1], Color.White, Color.Cyan, CoordinateProvider.GetCoordinate('B', 4));
AssertMove(c.Item2[2], Color.Red, Color.Blue, CoordinateProvider.GetCoordinate('G', 4));
AssertMove(c.Item2[3], Color.Blue, Color.Red, CoordinateProvider.GetCoordinate('B', 7));
}
public void Cross_ParentsWithTwoGenes_Cross()
{
var target = new TwoPointCrossover(0, 1);
var chromosome1 = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome1.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
chromosome1.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(4));
var chromosome2 = MockRepository.GenerateStub <ChromosomeBase>(4);
chromosome2.ReplaceGenes(0, new Gene[]
{
new Gene(5),
new Gene(6),
new Gene(7),
new Gene(8)
});
chromosome2.Expect(c => c.CreateNew()).Return(MockRepository.GenerateStub <ChromosomeBase>(4));
var actual = target.Cross(new List <IChromosome>()
{
chromosome1, chromosome2
});
Assert.AreEqual(2, actual.Count);
Assert.AreEqual(4, actual[0].Length);
Assert.AreEqual(4, actual[1].Length);
Assert.AreEqual(1, actual[0].GetGene(0).Value);
Assert.AreEqual(6, actual[0].GetGene(1).Value);
Assert.AreEqual(3, actual[0].GetGene(2).Value);
Assert.AreEqual(4, actual[0].GetGene(3).Value);
Assert.AreEqual(5, actual[1].GetGene(0).Value);
Assert.AreEqual(2, actual[1].GetGene(1).Value);
Assert.AreEqual(7, actual[1].GetGene(2).Value);
Assert.AreEqual(8, actual[1].GetGene(3).Value);
}
public IChromosome Solve(List <Span> spans)
{
var selection = new RouletteWheelSelection();
var crossover = new TwoPointCrossover();
var mutation = new UniformMutation();
var fitness = new Fitness();
var chromosome = new Chromosome(spans);
var population = new Population(100, 100, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
ga.CrossoverProbability = 0.8F;
ga.MutationProbability = 0.1F;
ga.Start();
return(ga.BestChromosome);
}
static void Main(string[] args)
{
int vezirSayisi = 8;
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome(vezirSayisi);
var population = new Population(5000, 5500, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessThresholdTermination(vezirSayisi * (vezirSayisi - 1) / 2)
};
int index = 0;
ga.GenerationRan += delegate
{
var bestChromosome = ga.Population.BestChromosome;
Console.Write("Index: " + index);
Console.Write(", Fitness: {0}", bestChromosome.Fitness);
Console.Write(", Genes: {0}", string.Join("-", bestChromosome.GetGenes()));
Console.WriteLine();
index++;
};
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
Console.Read();
}
public void Run(ref double Minfitness, ref Point startPoint, ref Point endPoint)
{
if (linePointArr.Length == 0)
{
return;
}
else
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new MyMutation();
fitness = new MyProblemFitness2(linePointArr, startPoint, endPoint);
var chromosome = new MyProblemChromosome2(linePointArr.Length < 3 ? 3 : linePointArr.Length);
var population = new Population(linePointArr.Length + 3, linePointArr.Length + 3, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new TimeEvolvingTermination(TimeSpan.FromMilliseconds(100)),
MutationProbability = 2f, //KS:变异概率50%
CrossoverProbability = 0.5f, //KS:交配概率50%
};
int index = 0; //KS:计算代数
ga.GenerationRan += delegate
{
var bestChromosome = ga.Population.BestChromosome;
/* Console.Write("Index: " + index);
* Console.Write(", Fitness: {0}", bestChromosome.Fitness);
*
* Console.Write(", Genes: {0}", string.Join("-", bestChromosome.GetGenes()));
*
* Console.WriteLine();*/
index++;
};
/* Console.WriteLine("GA running...");
* Stopwatch SW = new Stopwatch();
* SW.Start();
*/
ga.Start();//调用GA线程
// SW.Stop();
//Console.Write(", Time: {0}", SW.ElapsedMilliseconds);
Console.WriteLine("Best solution found has {0} fitness : {1}.", string.Join("-", ga.Population.BestChromosome.GetGenes()), ga.Population.BestChromosome.Fitness);
}
fitness.Evaluate(ga.Population.BestChromosome); //KS: 评价基因的优劣
Minfitness = ga.Population.BestChromosome.Fitness.Value;
startPoint = fitness.start.ToPoint();
endPoint = fitness.end.ToPoint();
/*
* if (ga.Population.BestChromosome.Fitness > Minfitness)
* {
* Minfitness = ga.Population.BestChromosome.Fitness.Value;
* startPoint = fitness.start.ToPoint();
* endPoint = fitness.end.ToPoint();
* }
* else
* {
* Minfitness = math.lerp((float)Minfitness, (float)ga.Population.BestChromosome.Fitness, 0.5f);
* }
*/
}
public static wynikGA Genetic(int counter, Item[] dane, double back_volume)
{
double[] minValue = new double[counter];
double[] maxValue = new double[counter];
int[] totalBits = new int[counter];
int[] fractionDigits = new int[counter];
for (int i = 0; i < counter; i++)
{
minValue[i] = 0;
maxValue[i] = 1;
totalBits[i] = 16;
fractionDigits[i] = 4;
}
var selection = new TournamentSelection(50, true);
var crossover = new TwoPointCrossover();
var mutation = new FlipBitMutation();
var chromosome = new FloatingPointChromosome(minValue, maxValue, totalBits, fractionDigits);
// var fitobj = new MyProblemFitness(dane, chromosome, back_volume);
//var fitness = fitobj.Evaluate;
var population = new Population(500, 500, chromosome);
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
//var _items = dane;
//var _bvol = back_volume;
/* var values = fc.ToFloatingPoints();
* var x1 = values[0];
* var y1 = values[1];
* var x2 = values[2];
* var y2 = values[3];
* return Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2));
*
*/
//chromosome.ReplaceGenes(0, chromosome.GetGenes());
double[] proc_wag = fc.ToFloatingPoints();
double suma_spakowanych = 0;
double wartosc_spakowanych = 0;
for (int i = 0; i < proc_wag.Length; i++)
{
suma_spakowanych += dane[i].item_volume * proc_wag[i];
wartosc_spakowanych += dane[i].item_value * proc_wag[i];
}
if (suma_spakowanych <= back_volume)
{
double temp = (wartosc_spakowanych * (suma_spakowanych / back_volume));
if (temp < 0)
{
if (Experiments.doOutput)
{
System.Console.WriteLine("LOL ");
}
}
}
return((suma_spakowanych <= back_volume) ? (wartosc_spakowanych * (suma_spakowanych / back_volume)) : (-suma_spakowanych));
});
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
if (Experiments.doOutput)
{
System.Console.WriteLine("GA running...");
}
ga.MutationProbability = 0.05f;
ga.CrossoverProbability = 0.20f;
//ga.Selection.
ga.Start();
// FITNESS if (Experiments.doOutput) System.Console.WriteLine("Best solution found has fitness = " + ga.BestChromosome.Fitness);
wynikGA w = new wynikGA();
w.ga = ga;
w.ch = chromosome;
w.ch.ReplaceGenes(0, ga.BestChromosome.GetGenes());
return(w);
}
public IList <IChromosome> TwoPointCrossover()
{
var target = new TwoPointCrossover();
return(target.Cross(CreateTwoParents()));
}
public void TwoPointCrossover()
{
IChromosome<int> a = new DigitalChromosome().GenerateFromArray(new int[] { 1, 2, 3, 4 });
IChromosome<int> b = new DigitalChromosome().GenerateFromArray(new int[] { 4, 3, 2, 1 });
TwoPointCrossover<int> cross = new TwoPointCrossover<int>(1, 3, 4);
IChromosome<int>[] res = cross.Crossover(a, b);
IChromosome<int>[] exp = new IChromosome<int>[2] { new DigitalChromosome().GenerateFromArray(new int[] { 1, 3, 2, 4 }), new DigitalChromosome().GenerateFromArray(new int[] { 4, 2, 3, 1 }) };
CollectionAssert.AreEqual(res, exp);
}
private void btnStart_Click(object sender, EventArgs e)
{
Models.Instance i = new Models.Instance();
i.Days = 7;
i.Doctors = doctors;
int min = (int)numMin.Value;
int max = (int)numMax.Value;
var chromosome = new Models.Chromosome(21, i, r);
var population = new Population(min, max, chromosome);
var fitness = new Models.Fitness();
IMutation mutation = new TworsMutation();
ISelection selection = new RouletteWheelSelection();
ICrossover crossover = new OnePointCrossover(r.Next(20));
if (cbxMutation.SelectedItem.ToString() == "Insertion")
{
mutation = new InsertionMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Partial Shuffle")
{
mutation = new PartialShuffleMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Reverse Sequence")
{
mutation = new ReverseSequenceMutation();
}
if (cbxSelection.SelectedItem.ToString() == "Elitizam")
{
selection = new EliteSelection();
}
if (cbxCrossover.SelectedItem.ToString() == "Two-point")
{
int p1 = r.Next(19);
int p2 = r.Next(p1 + 1, 20);
crossover = new TwoPointCrossover(p1, p2);
}
else if (cbxCrossover.SelectedItem.ToString() == "Uniform")
{
crossover = new UniformCrossover();
}
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ITermination termination = new FitnessStagnationTermination(50);
if (cbxTermination.SelectedItem.ToString() == "Generation number")
{
termination = new GenerationNumberTermination(200);
}
ga.Termination = termination;
ga.MutationProbability = (float)numProbability.Value;
ga.Start();
Gene[] g = ga.BestChromosome.GetGenes();
dataView.Rows.Clear();
for (int j = 0; j < 7; j++)
{
string[] row = new string[] { ((List <int>)g[j * 3].Value)[0].ToString() + " " + ((List <int>)g[j * 3].Value)[1].ToString(),
((List <int>)g[j * 3 + 1].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 1].Value)[1].ToString(),
((List <int>)g[j * 3 + 2].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 2].Value)[1].ToString() };
dataView.Rows.Add(row);
dataView.Rows[j].HeaderCell.Value = (j + 1).ToString();
}
lblFitness.Text = ga.BestChromosome.Fitness.ToString() + " , generacija broj " + ga.GenerationsNumber.ToString();
}
public static void CorretionCentroid()
{
//中心点数据
Stopwatch sw = new Stopwatch();
sw.Start();
List <Coordinate> centroidPoints = new List <Coordinate>(80);
StreamReader sr = new StreamReader(@"D:\MagicSong\OneDrive\2017研究生毕业设计\数据\项目用数据\中心点80.txt");
sr.ReadLine();//读取标题行
while (!sr.EndOfStream)
{
string[] line = sr.ReadLine().Split(',');
centroidPoints.Add(new Coordinate(double.Parse(line[1]), double.Parse(line[2])));
}
sr.Close();
//Bus数据,并且构造KD树
KdTree myKdtree = new KdTree(2);
IFeatureSet busFS = FeatureSet.Open(@"D:\MagicSong\OneDrive\2017研究生毕业设计\数据\项目用数据\BusStopGauss.shp");
List <Coordinate> busStopPoints = new List <Coordinate>(busFS.NumRows());
foreach (var item in busFS.Features)
{
var c = item.Coordinates[0];
busStopPoints.Add(c);
myKdtree.Insert(new double[] { c.X, c.Y }, item);
}
Console.WriteLine("数据读取完毕，开始构造遗传算法");
IFeatureSet newCentroid = new FeatureSet(FeatureType.Point);
newCentroid.Name = "优化过的中心点";
newCentroid.Projection = ProjectionInfo.FromEpsgCode(GAUSS_EPSG);
newCentroid.DataTable.Columns.Add("name", typeof(string));
//遗传算法，构造适应性函数
MyProblemChromosome.CandiateNumber = 5;
List <int[]> candinatesForEachControid = new List <int[]>(centroidPoints.Count);
foreach (var item in centroidPoints)
{
object[] nearest = myKdtree.Nearest(new double[] { item.X, item.Y }, MyProblemChromosome.CandiateNumber);
candinatesForEachControid.Add(nearest.Select((o) =>
{
var f = o as IFeature;
return(f.Fid);
}).ToArray());
}
MyProblemFitness fitness = new MyProblemFitness(centroidPoints, busStopPoints, candinatesForEachControid);
MyProblemChromosome mpc = new MyProblemChromosome(centroidPoints.Count);
//这边可以并行
MyProblemChromosome globalBest = null;
Console.WriteLine("遗传算法构造已经完成！");
sw.Stop();
Console.WriteLine("一共用时：{0}s", sw.Elapsed.TotalSeconds);
int GACount = 8;
Parallel.For(0, GACount, new Action <int>((index) =>
{
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new ReverseSequenceMutation();
var population = new Population(1000, 1200, mpc);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(1000);
Stopwatch sw1 = new Stopwatch();
sw1.Start();
Console.WriteLine("遗传算法任务{0}正在运行.......", index);
ga.Start();
var best = ga.BestChromosome as MyProblemChromosome;
if (globalBest == null || globalBest.Fitness < best.Fitness)
{
globalBest = best;
}
sw1.Stop();
Console.WriteLine("第{0}次遗传算法已经完成，耗费时间为：{1}s，最终的fitness为：{2},有效个数为：{3}", index, sw1.Elapsed.TotalSeconds, best.Fitness, best.Significance);
}));
Console.WriteLine("Final Choose!");
Console.WriteLine("最终的fitness为：{0},有效个数为：{1}", globalBest.Fitness, globalBest.Significance);
for (int i = 0; i < globalBest.Length; i++)
{
int index = candinatesForEachControid[i][(int)globalBest.GetGene(i).Value];
Coordinate c = busStopPoints[index];
var f = newCentroid.AddFeature(new Point(c));
f.DataRow.BeginEdit();
f.DataRow["name"] = busFS.GetFeature(index).DataRow["name"];
f.DataRow.EndEdit();
}
newCentroid.SaveAs("newCentroid.shp", true);
Console.ReadKey();
}
private static IAlgoritmo CriaAlgoritmoGenetico(Dictionary <string, string[]> dict, List <string> flat, Problema problema)
{
int populacaoMin, populacaoMax;
IPopulation population;
ISelection selection;
ICrossover crossover;
IMutation mutation;
ITermination termination;
IReinsertion reinsertion;
float crossoverProbability, mutationProbability;
var p = dict.ValueOrDefault("p", "50,100").Split(new[] { ',' });
if (p.Length != 2 || !int.TryParse(p[0], out populacaoMin) || !int.TryParse(p[1], out populacaoMax))
{
throw new ArgumentException("Faixa de população inválida.");
}
population = new Population(populacaoMin, populacaoMax, new CromossomoViajante(problema.Mapa.Locais.Count));
switch (dict.ValueOrDefault("s", "t"))
{
case "e":
selection = new EliteSelection();
break;
case "r":
selection = new RouletteWheelSelection();
break;
case "s":
selection = new StochasticUniversalSamplingSelection();
break;
case "t":
selection = new TournamentSelection();
break;
default:
throw new ArgumentException("Seleção inválida.");
}
switch (dict.ValueOrDefault("c", "o"))
{
case "s":
crossover = new CutAndSpliceCrossover();
break;
case "c":
crossover = new CycleCrossover();
break;
case "o":
crossover = new OrderedCrossover();
break;
case "ob":
crossover = new OrderBasedCrossover();
break;
case "op":
crossover = new OnePointCrossover();
break;
case "pm":
crossover = new PartiallyMappedCrossover();
break;
case "p":
crossover = new PositionBasedCrossover();
break;
case "tpa":
crossover = new ThreeParentCrossover();
break;
case "tp":
crossover = new TwoPointCrossover();
break;
case "u":
crossover = new UniformCrossover();
break;
default:
throw new ArgumentException("Crossover inválido.");
}
switch (dict.ValueOrDefault("m", "r"))
{
case "d":
mutation = new DisplacementMutation();
break;
case "f":
mutation = new FlipBitMutation();
break;
case "i":
mutation = new InsertionMutation();
break;
case "s":
mutation = new PartialShuffleMutation();
break;
case "r":
mutation = new ReverseSequenceMutation();
break;
case "t":
mutation = new TworsMutation();
break;
case "u":
mutation = new UniformMutation();
break;
default:
throw new ArgumentException("Mutação inválida.");
}
switch (dict.ValueOrDefault("t", "s"))
{
case "s":
termination = new FitnessStagnationTermination();
break;
case "t":
termination = new FitnessThresholdTermination();
break;
case "g":
termination = new GenerationNumberTermination();
break;
default:
throw new ArgumentException("Terminação inválida.");
}
switch (dict.ValueOrDefault("e", "e"))
{
case "e":
reinsertion = new ElitistReinsertion();
break;
case "p":
reinsertion = new PureReinsertion();
break;
case "u":
reinsertion = new UniformReinsertion();
break;
default:
throw new ArgumentException("Reinserção inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("cp", "0,75"), out crossoverProbability))
{
throw new ArgumentException("Probabilidade de crossover inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("mp", "0,25"), out mutationProbability))
{
throw new ArgumentException("Probabilidade de mutação inválida.");
}
return(new AlgoritmoGenetico(problema, population, selection, crossover, crossoverProbability, mutation, mutationProbability, termination, reinsertion));
}
private void EvolveGeneticStrategyButton_Click(object sender, RoutedEventArgs e)
{
OutputTextBlock.Text = "Evolving...";
Task.Run(() =>
{
var chromosome = new BlackjackChromosome();
var fitness = new BlackjackFitness();
var population = new Population(Settings.GeneticSettings.MinPopulationSize, Settings.GeneticSettings.MaxPopulationSize, chromosome);
ISelection selection;
switch (Settings.GeneticSettings.SelectionType)
{
case SelectionType.Elite:
selection = new EliteSelection();
break;
case SelectionType.RouletteWheel:
selection = new RouletteWheelSelection();
break;
case SelectionType.StochasticUniversalSampling:
selection = new StochasticUniversalSamplingSelection();
break;
case SelectionType.Tournament:
selection = new TournamentSelection(Settings.GeneticSettings.TournamentSize);
break;
default:
throw new InvalidOperationException();
}
ICrossover crossover;
switch (Settings.GeneticSettings.CrossoverType)
{
case CrossoverType.AlternatingPosition:
crossover = new AlternatingPositionCrossover();
break;
case CrossoverType.CutAndSplice:
crossover = new CutAndSpliceCrossover();
break;
case CrossoverType.Cycle:
crossover = new CycleCrossover();
break;
case CrossoverType.OnePoint:
crossover = new OnePointCrossover();
break;
case CrossoverType.TwoPoint:
crossover = new TwoPointCrossover();
break;
case CrossoverType.OrderBased:
crossover = new OrderBasedCrossover();
break;
case CrossoverType.Ordered:
crossover = new OrderedCrossover();
break;
case CrossoverType.PartiallyMapped:
crossover = new PartiallyMappedCrossover();
break;
case CrossoverType.PositionBased:
crossover = new PositionBasedCrossover();
break;
case CrossoverType.ThreeParent:
crossover = new ThreeParentCrossover();
break;
case CrossoverType.Uniform:
crossover = new UniformCrossover(Settings.Current.GeneticSettings.MixProbability);
break;
case CrossoverType.VotingRecombination:
crossover = new VotingRecombinationCrossover();
break;
default:
throw new InvalidOperationException();
}
var mutation = new UniformMutation();
var termination = new FitnessStagnationTermination(Settings.Current.GeneticSettings.NumStagnantGenerations);
var taskExecutor = new ParallelTaskExecutor();
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.TaskExecutor = taskExecutor;
ga.MutationProbability = Settings.GeneticSettings.MutationProbability;
ga.CrossoverProbability = Settings.GeneticSettings.CrossoverProbability;
var latestFitness = double.MinValue;
ga.GenerationRan += (s, o) =>
{
geneticStrategy = (IStrategy)ga.BestChromosome;
var generationNumber = ga.GenerationsNumber;
var bestFitness = ga.BestChromosome.Fitness.Value;
var avgFitness = ga.Population.CurrentGeneration.Chromosomes.Average(c => c.Fitness.Value);
Dispatcher.Invoke(() =>
{
if (generationNumber == 1)
{
OutputTextBlock.Text = string.Empty;
}
OutputTextBlock.Text = $"Gen: {generationNumber}\tFit: {bestFitness}\tAvg: {avgFitness.ToString("0")}\n" + OutputTextBlock.Text;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var savedImageName = Settings.Current.GeneticSettings.SaveImagePerGeneration ? "gen" + generationNumber : null;
StrategyViewer.Draw(GeneticStrategyCanvas, geneticStrategy, $"Best from generation {generationNumber}", savedImageName);
}
}, DispatcherPriority.Background);
};
ga.TerminationReached += (s, o) =>
{
Dispatcher.Invoke(() =>
{
OutputTextBlock.Text = "Termination reached.\n" + OutputTextBlock.Text;
TestGeneticStrategyButton.IsEnabled = true;
}, DispatcherPriority.Background);
};
ga.Start();
});
}
public static void Main(string[] args)
{
string plaintextFilename = "text_sample.txt";
string logFilename = "log.txt";
string key = "cipherkey";
int populationSize = 100;
int offspring = 10;
float mutationProbability = 0.25f;
float crossoverProbability = 0.85f;
int maxGenerations = 500;
using (StreamWriter w = File.AppendText($"../../../logs/{logFilename}"))
{
//read in plaintext
string plaintext = File.ReadAllText($"../../../statistics/text/{plaintextFilename}").ToLower();
//init cipher and encipher plaintext
ICipher cipher = new VigenereCipher(plaintext);
cipher.Encipher(key);
Console.WriteLine(cipher.CipherText);
//init genetic alorithm operators
//use tournament selection to minimize the likelyhood of staying in a lokal optima
var selection = new TournamentSelection(5);
//crossover the solution candidates by choosing a interval of the key and swap the interval
var crossover = new TwoPointCrossover();
//mutate the solution candidates with the own implemented mutation operator
var mutation = new CipherMutation();
//evaluate the fitness of the solution candidates with the own implemented fitness function
var fitness = new CipherFitness(cipher);
//initalize the solution candidates with the own implemented solution candidate implementation
var chromosome = new CipherChromosome(key.Length);
//initialize a population
var population = new Population(populationSize, populationSize + offspring, chromosome);
//initialize the genetic algorithm with the above initialized operators
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
//set mutation and crossover probabilities
ga.MutationProbability = mutationProbability;
ga.CrossoverProbability = crossoverProbability;
//specify the algorithm termination with max generations or a fitness threshold
ga.Termination = new OrTermination(new GenerationNumberTermination(maxGenerations), new FitnessThresholdTermination(0.99));
var latestFitness = 0.0;
//hang into the GenerationRan event which gets called by the library after each generation ran.
ga.GenerationRan += (sender, e) =>
{
//select the best solution candidate in the current generation
var bestChromosome = ga.BestChromosome as CipherChromosome;
//if fitness in current generation is better than fitness in previous generations
if (bestChromosome.Fitness != latestFitness)
{
latestFitness = bestChromosome.Fitness.Value;
string genText = String.Format("Generation {0}: Best solution found is Key:{1} with {2} fitness.", ga.GenerationsNumber, bestChromosome.ToString(), bestChromosome.Fitness);
Console.WriteLine(genText);
w.WriteLine(genText);
}
};
string gaText = $"------Plaintextfile:{plaintextFilename}, Key:{key}------\n------Genetic Algorithm Settings: Populationsize:{populationSize}, Mutation:{mutationProbability}, Crossover:{crossoverProbability}------";
Console.WriteLine(gaText);
w.WriteLine(gaText);
//start ga
ga.Start();
var finalSolution = ga.BestChromosome as CipherChromosome;
string doneText = $"GA done in {ga.GenerationsNumber} generations.\n Best solution found is Key:{finalSolution.ToString()} (L={finalSolution.ToString().Length}) with {finalSolution.Fitness} fitness. ";
string decipheredText = cipher.Decipher(finalSolution.ToString());
Console.WriteLine(doneText + "\n" + decipheredText);
w.WriteLine(doneText + "\n" + decipheredText);
}
}