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 GA_Issue61_Solved()
{
const Int32 FinalAns = 4567213;
var chromosome = new Issue61.GuessNumberChromosome(FinalAns.ToString().Length);
var population = new Population(1000, 5000, chromosome);
var fitness = new Issue61.GuessNumberFitness(FinalAns);
var selection = new EliteSelection();
var crossover = new AlternatingPositionCrossover();
var mutation = new ReverseSequenceMutation();
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.MutationProbability = 0.2f;
ga.CrossoverProbability = 0.75f;
ga.Termination = new OrTermination(
new FitnessThresholdTermination(1.0),
new FitnessStagnationTermination(200),
new GenerationNumberTermination(1000));
ga.Population.GenerationStrategy = new TrackingGenerationStrategy();
ga.Start();
foreach (var gen in ga.Population.Generations)
{
foreach (var chromossome in gen.Chromosomes)
{
// Asserts if AlternatingPositionCrossover generated only ordered chromossomes.
Assert.AreEqual(chromosome.Length, chromosome.GetGenes().Distinct().Count());
}
}
}
public void Mutate_NoProbality_NoReverseSequence()
{
var target = new ReverseSequenceMutation();
var chromosome = Substitute.For <ChromosomeBase>(4);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
});
var rnd = Substitute.For <IRandomization>();
rnd.GetDouble().Returns(0.1);
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 0);
Assert.AreEqual(4, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(2, chromosome.GetGene(1).Value);
Assert.AreEqual(3, chromosome.GetGene(2).Value);
Assert.AreEqual(4, chromosome.GetGene(3).Value);
}
public void Mutate_ValidChromosome_ReverseSequence()
{
var target = new ReverseSequenceMutation();
var chromosome = Substitute.For <ChromosomeBase>(6);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(2),
new Gene(3),
new Gene(4),
new Gene(5),
new Gene(6),
});
var rnd = Substitute.For <IRandomization>();
rnd.GetUniqueInts(2, 0, 6).Returns(new int[] { 1, 4 });
RandomizationProvider.Current = rnd;
target.Mutate(chromosome, 1);
Assert.AreEqual(6, chromosome.Length);
Assert.AreEqual(1, chromosome.GetGene(0).Value);
Assert.AreEqual(5, chromosome.GetGene(1).Value);
Assert.AreEqual(4, chromosome.GetGene(2).Value);
Assert.AreEqual(3, chromosome.GetGene(3).Value);
Assert.AreEqual(2, chromosome.GetGene(4).Value);
Assert.AreEqual(6, chromosome.GetGene(5).Value);
}
public static void ga()
{
AlgorithmList.loadformnistfsgm();
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyChromosome();
var population = new Population(4, 6, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(4);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
var a = ga.BestChromosome.GetGenes();
string s = "";
foreach (var b in a)
{
s = s + b.Value.ToString();
}
Console.WriteLine("Best solution found has {0} ", s);
}
private void Optimize_Load(object sender, EventArgs e)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TrajectoryFitness();
var chromosome = new TrajectoryChromosome(TrajectoryChromosome.ChromosomeLength(3), 3);
var population = new Population(35, 40, chromosome);
//72 (6)
//67 (2)
//49 (3)
//102 (1)
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
var executor = new SmartThreadPoolTaskExecutor();
executor.MinThreads = 1;
executor.MaxThreads = 1;
ga.TaskExecutor = executor;
ga.Termination = new GenerationNumberTermination(10);
Console.WriteLine("GA running...");
var t = DateTime.Now;
ga.Start();
Console.WriteLine("Time:" + DateTime.Now.Subtract(t).TotalSeconds);
Console.WriteLine("Best solution found has {0} fitness.", int.MaxValue - ga.BestChromosome.Fitness);
Console.WriteLine(ga.BestChromosome.GetGene(0).Value + " " + ga.BestChromosome.GetGene(1).Value + " " + ga.BestChromosome.GetGene(2).Value);
}
public IMutation ReverseSequenceMutation()
{
var target = new ReverseSequenceMutation();
target.Mutate(new TspChromosome(_numberOfCities), _probability);
return(target);
}
static GeneticAlgorithm CreateGA(IPopulation population)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new TspFitness(100, 0, 1000, 0, 1000);
return(new GeneticAlgorithm(population, fitness, selection, crossover, mutation));
}
public void Mutate_LessThanThreeGenes_Exception()
{
var target = new ReverseSequenceMutation();
var chromosome = Substitute.For <ChromosomeBase>(2);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
});
Assert.Catch <MutationException>(() =>
{
target.Mutate(chromosome, 0);
}, "A chromosome should have, at least, 3 genes. {0} has only 2 gene.".With(chromosome.GetType().Name));
}
public void Initialize(int numberOfCities, int areaWidth, int areaHeight)
{
Stop();
Fitness = new TspFitness(numberOfCities, areaWidth, areaHeight);
var chromosome = new TspChromosome(numberOfCities);
// This operators are classic genetic algorithm operators that lead to a good solution on TSP,
// but you can try others combinations and see what result you get.
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
_ga = new GeneticAlgorithm(population, Fitness, selection, crossover, mutation);
}
public void LinesOfCode()
{
const string source = @"
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Diagnostics;
namespace ConsoleApplication1
{
class Calculations
{
double CalculateTotal(int quantity, int itemPrice)
{
double basePrice = quantity * itemPrice;
if (basePrice > 1000)
{
return basePrice * 0.95;
}
else
{
return basePrice * 0.98;
}
}
}
}";
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MetricsFitness <LinesOfCodeMetrics>();
var chromosome = new RefactoringChromosome(3, source);
var population = new Population(20, 30, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(50)
};
ga.Start();
Assert.IsNotNull(ga.BestChromosome.Fitness);
TestContext.WriteLine($"Best fitness is {ga.BestChromosome.Fitness} and source looks like: \n {ga.BestChromosome}");
}
public void StartGeneticAlgo(string target)
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyFitness {
Target = target
};
var chromosome = new Chromosome(target);
var population = new Population(500, 1000, chromosome);
_stopwatch = Stopwatch.StartNew();
_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(300),
MutationProbability = float.MaxValue
};
_timer = new Timer(new TimerCallback(_ =>
{
_ga.Termination = new GenerationNumberTermination(_ga.GenerationsNumber + 1);
if (_ga.GenerationsNumber > 0)
{
_ga.Resume();
}
else
{
_ga.Start();
}
var teste = string.Empty;
for (var i = 0; i < _ga.BestChromosome.GetGenes().Length; i++)
{
teste += _ga.BestChromosome.GetGenes()[i].Value;
if (teste == target)
{
Stop();
_stopwatch.Stop();
}
}
StateHasChanged?.Invoke();
}), null, 0, 1);
}
static void Main(string[] args)
{
Console.WriteLine("Hello World!");
var selection = new GeneticSharp.Domain.Selections.RouletteWheelSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome();
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine("Best solution found has {0} fitness.", ga.BestChromosome.Fitness);
}
private GeneticAlgorithm CreateGA(Func <TspChromosome, Population> createPopulation = null)
{
var selection = new EliteSelection();
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var chromosome = new TspChromosome(_numberOfCities);
var fitness = new TspFitness(_numberOfCities, 0, 1000, 0, 1000);
var population = createPopulation == null
? new Population(_minPopulationSize, _minPopulationSize, chromosome)
: createPopulation(chromosome);
population.GenerationStrategy = new PerformanceGenerationStrategy();
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(_generations)
};
return(ga);
}
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 OutputData DoIt(InputData inputData)
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();;; //OnePointCrossover
var mutation = new ReverseSequenceMutation(); //DisplacementMutation ReverseSequenceMutation
var fitness = new MyProblemFitness(inputData);
var chromosome = new MyProblemChromosome(inputData.F, inputData.N);
var population = new Population(100, 1000, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessStagnationTermination(10000);
ga.Population.GenerationStrategy = new TrackingGenerationStrategy();
Console.WriteLine("GA running...");
double currentFitness = 0;
ga.GenerationRan += delegate(object obj, EventArgs eventArgs)
{
var outputx = new MyProblemFitness(inputData);
var re = outputx.Evaluate(((GeneticAlgorithm)obj).BestChromosome);
if (re != currentFitness)
{
currentFitness = re;
Console.WriteLine("Fitness..." + re);
}
};
ga.Start();
var x = ga.BestChromosome;
var output = new MyProblemFitness(inputData);
var res = output.Evaluate(ga.BestChromosome);
Console.WriteLine("Done...fitness..." + res);
return(output.OutputData);
}
public void TSPSolve(int numberOfPoints, List <Point> stationList)
{
var fitness = new TspFitness(stationList);
var chromosome = new TspChromosome(numberOfPoints);
var crossover = new OrderedCrossover();
var mutation = new ReverseSequenceMutation();
var selection = new RouletteWheelSelection();
var population = new Population(50, 100, chromosome);
m_ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
m_ga.Termination = new FitnessStagnationTermination(100);
m_ga.TaskExecutor = new ParallelTaskExecutor {
MinThreads = 100,
MaxThreads = 200
};
m_ga.GenerationRan += delegate {
var distance = ((TspChromosome)m_ga.BestChromosome).Distance;
};
m_ga.TerminationReached += delegate {
var c = m_ga.Population.CurrentGeneration.BestChromosome as TspChromosome;
var genes = c.GetGenes();
var points = ((TspFitness)m_ga.Fitness).Points;
finalPoints = new List <Point>();
for (int i = 0; i < genes.Length; i++)
{
finalPoints.Add(points[(int)genes[i].Value]);
}
tspSolved();
};
m_gaThread = new Thread(() => m_ga.Start());
m_gaThread.Start();
}
static void Main(string[] args)
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome();
var population = new Population(20, 20, chromosome);
var ga = new GeneticSharp.Domain.GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(100)
};
Console.WriteLine("GA running...");
ga.Start();
if (ga.BestChromosome.Fitness != null)
{
double fitnessValue = ga.BestChromosome.Fitness.Value;
string solution = ga.BestChromosome.ToUserFriendly();
Console.WriteLine($"Best solution {solution} found has {fitnessValue} fitness for {ga.GenerationsNumber} generations.");
}
}
static void Main(string[] args)
{
SQLiteConnection dbConnect;
SQLiteCommand cmd;
SQLiteDataReader dbread = null;
string inputstring;
string cmc = ""; string power = "";
StreamReader inputReader = new StreamReader("input.txt");
dbConnect = new SQLiteConnection("Data Source=C://Users/Korik/Documents/Visual Studio 2015/Projects/GAdeckbuilder/GAdeckbuilder/bin/Debug/mtg.db;");
dbConnect.Open();
string sql;
bool white = false, blue = false, black = false, red = false, green = false;
while (inputReader.Peek() != -1)
{
inputstring = inputReader.ReadLine();
sql = "SELECT * FROM cards INNER JOIN colors ON cards.name = colors.name where cards.setcode = 'BFZ' AND colors.setcode = 'BFZ' AND cards.name = '" + inputstring + "';";
// Console.WriteLine(sql);
cmd = new SQLiteCommand(sql, dbConnect);
dbread = cmd.ExecuteReader();
while (dbread.Read())
{
//Console.WriteLine(dbread[1].ToString());
white = false; blue = false; black = false; red = false; green = false;
if (Convert.ToBoolean(dbread["white"].ToString()))
{
white = true;
}
if (Convert.ToBoolean(dbread["blue"].ToString()))
{
blue = true;
}
if (Convert.ToBoolean(dbread["black"].ToString()))
{
black = true;
}
if (Convert.ToBoolean(dbread["red"].ToString()))
{
red = true;
}
if (Convert.ToBoolean(dbread["green"].ToString()))
{
green = true;
}
// Console.WriteLine(dbread["cmc"].ToString());
cmc = dbread["cmc"].ToString();
//Console.WriteLine(cmc);
power = dbread["PowerLevel"].ToString();
//Console.WriteLine(power);
}
// Console.WriteLine(inputstring + white + blue + red + black + green + cmc + power);
CardPool.Add(new Card(inputstring, white, blue, black, red, green, int.Parse(cmc), int.Parse(power)));
}
inputReader.Close();
dbread.Close();
dbConnect.Close();
foreach (Card c in CardPool)
{
// Console.WriteLine(c.White);
}
Console.ReadLine();
var selection = new EliteSelection();
var crossover = new OnePointCrossover();
var mutation = new ReverseSequenceMutation();
var fitness = new DeckFitness();
var chromosome = new DeckChromosome();
var population = new Population(400, 400, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessStagnationTermination(4000);
ga.CrossoverProbability = 0.80f;
ga.MutationProbability = 1.00f;
Console.WriteLine("GA running...");
ga.Start();
printDecklist(ga.BestChromosome);
Console.WriteLine("Number of Generations " + ga.GenerationsNumber);
Console.ReadLine();
Console.ReadLine();
}
public void Start()
{
var w = new StreamWriter("C:\\Users\\Taylor\\source\\repos\\ComboProject\\ComboProject\\bin\\Debug\\genelog.csv");
int gen = 1;
// init chromosomes
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
new double[] { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },
new int[] { 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20 },
new int[] { 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5 });
// gen population
var population = new Population(50, 100, chromosome);
// fitness function
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var genes = fc.ToFloatingPoints();
string combo = combogen.generateGeneticCombo(genes, 0);
int damage = ComboSimulator.getComboDamage(combo, movelist);
w.WriteLine(string.Format("{0},{1},{2},,{3},{4},{5},{6},{7},{8},{9},{10},{11},{12}",
gen.ToString(),
combo,
damage.ToString(),
genes[0],
genes[1],
genes[2],
genes[3],
genes[4],
genes[5],
genes[6],
genes[7],
genes[8],
genes[9]));
w.Flush();
return(damage);
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new ReverseSequenceMutation();
var termination = new FitnessStagnationTermination(5000);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.MutationProbability = 0.7f;
Console.WriteLine("Generation: combo = damage");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as FloatingPointChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
string combo = combogen.generateGeneticCombo(bestChromosome.ToFloatingPoints(), 0);
Console.WriteLine("Generation {0,2}: {1} = {2}",
ga.GenerationsNumber,
combo,
bestFitness
);
}
gen++;
};
ga.Start();
Console.ReadKey();
}
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();
}
