public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new TworsMutation();
var chromosome = new BitmapChromosome(32, 32);
var targetBitmap = new Bitmap(32, 32);
var fitness = new BitmapEqualityFitness(targetBitmap);
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = new ParallelTaskExecutor()
{
MinThreads = 10,
MaxThreads = 20
};
ga.Termination = new GenerationNumberTermination(5);
ga.Start();
var c = ga.BestChromosome as BitmapChromosome;
Assert.IsNotNull(c);
var bitmap = c.BuildBitmap();
Assert.IsNotNull(bitmap);
Assert.AreEqual(32, bitmap.Width);
Assert.AreEqual(32, bitmap.Height);
}
public GeneticAlgorithm CreateGA(Func <RobotChromosome, Task <ContestGame> > contestFactory, RobotEvolutionConfiguration config)
{
NumberOfSimultaneousEvaluations = 2;
var fitness = new RobotFitness(contestFactory, config);
var chromosome = new RobotChromosome(config);
var crossover = new UniformCrossover();
var mutation = new FlipBitMutation();
var selection = new EliteSelection();
var population = new Population(NumberOfSimultaneousEvaluations, NumberOfSimultaneousEvaluations, chromosome)
{
GenerationStrategy = new PerformanceGenerationStrategy()
};
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new RobotTermination(),
//TaskExecutor = new LinearTaskExecutor(),
TaskExecutor = new ParallelTaskExecutor
{
MaxThreads = 10
}
};
ga.GenerationRan += delegate
{
Console.WriteLine("Generation complete");
};
ga.MutationProbability = .1f;
return(ga);
}
public void Start()
{
var fitness = new StringFitness(targetString);
var chromosome = new StringChromosome(targetString.Length);
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation();
var selection = new EliteSelection();
var population = new Population(1000, 1000, chromosome);
var termination = new FitnessThresholdTermination(1.00);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = termination;
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as StringChromosome;
var bestFitness = bestChromosome.Fitness.Value;
Console.WriteLine(
"Generation {0}: \n{1} \nFitness: {2}",
ga.GenerationsNumber,
bestChromosome.Sentence,
bestFitness
);
if (bestFitness > latestFitness)
{
latestFitness = bestFitness;
}
};
ga.Start();
}
public AnalysisResults Optimize(PixelStructure structure)
{
var chromosome = new StructuralChromosome(structure.Pixels.Count);
var population = new Population(5, 10, chromosome);
var fitness = new StructuralFitness(structure);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(); //FlipBitMutation();
var termination = new FitnessStagnationTermination(10);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.GenerationRan += GenerationRan;
ga.Start();
var structRes = ga.BestChromosome as StructuralChromosome;
return(structRes.Results);
}
public static Sudoku Eval(Sudoku sudoku, int populationSize, double fitnessThreshold, int generationNb)
{
//creation du chromosome
IChromosome chromosome = new SudokuPermutationsChromosome(sudoku);
var fitness = new SudokuFitness(sudoku);
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation();
var population = new Population(populationSize, populationSize, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new OrTermination(new ITermination[]
{
new FitnessThresholdTermination(fitnessThreshold),
new GenerationNumberTermination(generationNb)
})
};
ga.Start();
var bestIndividual = ((ISudokuChromosome)ga.Population.BestChromosome);
var solutions = bestIndividual.GetSudokus();
return(solutions[0]);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new AutoConfigChromosome();
var targetChromosome = new TspChromosome(10);
var targetFitness = new TspFitness(10, 0, 100, 0, 100);
var fitness = new AutoConfigFitness(targetFitness, targetChromosome);
fitness.PopulationMinSize = 20;
fitness.PopulationMaxSize = 20;
fitness.Termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(5));
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.TaskExecutor = new SmartThreadPoolTaskExecutor()
{
MinThreads = 10,
MaxThreads = 20
};
ga.Termination = new GenerationNumberTermination(2);
ga.Start();
Assert.NotNull(ga.BestChromosome);
}
private GeneticAlgorithm PrepareGeneticAlgorithm(string[][] board)
{
double[][] parsedBoard = this.ParseBoardToDouble(board);
var chromosome = new TicTacToeChromosome(parsedBoard);
var population = new Population(50, 100, chromosome);
var fitness = new TicTacToeFitness
{
Minimum = BOARD_SIZE
};
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation();
var termination = new OrTermination(new ITermination[]
{
new FitnessStagnationTermination(3000),
new FitnessThresholdTermination(4),
new FitnessThresholdTermination(3),
new FitnessThresholdTermination(2)
});
var geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = termination
};
#if DEBUG
geneticAlgorithm.GenerationRan += (sender, e) =>
{
var bestChromosome = geneticAlgorithm.BestChromosome as TicTacToeChromosome;
Console.WriteLine($"Generation { geneticAlgorithm.GenerationsNumber }: Position ({ bestChromosome.Position.X }, { bestChromosome.Position.Y }) = { bestChromosome.Fitness }");
};
#endif
return(geneticAlgorithm);
}
public GeneticSharpSolver Setup()
{
var chromosome = new FloatingPointChromosome(
_decisions.Select(d => d.LowerBound).ToArray(),
_decisions.Select(d => d.UpperBound).ToArray(),
_decisions.Select(d => d.TotalBits).ToArray(),
_decisions.Select(d => d.FractionDigits).ToArray());
var population = new Population(_populationSize, _populationSize + _offspringNumber, chromosome);
var fitness = new FuncFitness((c) => { return(_objective(c as FloatingPointChromosome, this)); });
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new GenerationNumberTermination(_maxGenerations);
_ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
_ga.Termination = termination;
if (_generationCallback != null)
{
_ga.GenerationRan += (sender, e) => _generationCallback(_ga);
}
return(this);
}
public static async Task Main(string[] args)
{
Console.WriteLine($"[{DateTime.Now}] Tuner start");
SettingsLoader.Init("settings.json");
_webService = new WebService();
_generationStopwatch = new Stopwatch();
await _webService.EnableIfAvailable();
_testId = await _webService.RegisterTest();
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(true);
var fitness = new EvaluationFitness(_testId, _webService);
var chromosome = new EvaluationChromosome();
var population = new Population(SettingsLoader.Data.MinPopulation, SettingsLoader.Data.MaxPopulation, chromosome);
var geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
geneticAlgorithm.Termination = new GenerationNumberTermination(SettingsLoader.Data.GenerationsCount);
geneticAlgorithm.GenerationRan += GeneticAlgorithm_GenerationRan;
_generationStopwatch.Start();
geneticAlgorithm.Start();
Console.WriteLine("Best solution found has {0} fitness.", geneticAlgorithm.BestChromosome.Fitness);
Console.ReadLine();
}
protected override GeneticAlgorithm CreateGA()
{
NumberOfSimultaneousEvaluations = SimulationsGrid.x * SimulationsGrid.y;
m_fitness = new CarFitness();
var chromosome = new CarChromosome(Config);
var crossover = new UniformCrossover();
var mutation = new FlipBitMutation();
var selection = new EliteSelection();
var population = new Population(NumberOfSimultaneousEvaluations, NumberOfSimultaneousEvaluations, chromosome)
{
GenerationStrategy = new PerformanceGenerationStrategy()
};
var ga = new GeneticAlgorithm(population, m_fitness, selection, crossover, mutation)
{
Termination = new CarTermination(),
TaskExecutor = new ParallelTaskExecutor
{
MinThreads = population.MinSize,
MaxThreads = population.MaxSize * 2
}
};
ga.GenerationRan += delegate
{
m_lastPosition = Vector3.zero;
m_evaluationPool.ReleaseAll();
};
ga.MutationProbability = .1f;
return(ga);
}
static void Main(string[] args)
{
var data = new Data();
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var fitness = new FitnessFunction(data);
var chromosome = new Chromosome(data.ItemsCount);
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(100);
Console.WriteLine("Rozpoczęcie wykonania algorytmu...");
var watch = System.Diagnostics.Stopwatch.StartNew();
ga.Start();
watch.Stop();
var executionTime = watch.ElapsedMilliseconds;
Console.WriteLine("---------Wynik---------");
var result = new Result(ga.BestChromosome, executionTime, data.ListOfItems);
result.DisplayResult();
Console.ReadLine();
}
public void InitiGA(GeneticAlgoritmConfig config)
{
Config = config;
//IChromosome adamChromosome = new FloatingPointChromosome(config.MinChromosomeValue, config.MaxChromosomeValue, config.ChromosomeSize, config.ChromosomeFractionDigits);
float maxWidth = 1000000;
float maxHeight = 1000000;
IChromosome adamChromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { maxWidth, maxHeight, maxWidth, maxHeight },
new int[] { 64, 64, 64, 64 },
new int[] { 2, 2, 2, 2 });
Population population = new Population(config.MinPopulationSize, config.MinPopulationSize, adamChromosome);
IFitness fitness = new SimpleFitness();
ISelection selection = new EliteSelection();
ICrossover crossover = new UniformCrossover(0.1f);
IMutation mutation = new FlipBitMutation();
ITermination termination = new FitnessStagnationTermination(config.StagnationGenerationCount);
population.GenerationStrategy = new PerformanceGenerationStrategy();
geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = termination
};
geneticAlgorithm.CrossoverProbability = config.CrossoverProbability;
geneticAlgorithm.MutationProbability = config.MutationProbability;
geneticAlgorithm.GenerationRan += OnNewGeneration;
geneticAlgorithm.TerminationReached += OnTermination;
geneticAlgorithm.Start();
}
/// <summary>
/// GeneticSharp Console Application template.
/// <see href="https://github.com/giacomelli/GeneticSharp"/>
/// </summary>
static void Main(string[] args)
{
// TODO: use the best genetic algorithm operators to your optimization problem.
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var fitness = new chessQueenFitness();
var chromosome = new chessQueenChromosome();
var population = new Population(5, 40, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
//ga.Termination = new FitnessStagnationTermination(4);
ga.Termination = new FitnessThresholdTermination(0);
ga.GenerationRan += (s, e) => Console.WriteLine($"Generation {ga.GenerationsNumber}. Best fitness: {ga.BestChromosome.Fitness.Value}");
ga.GenerationRan += (s, e) => Console.WriteLine($"Chromosome Sample 1:" + String.Join("", ga.Population.CurrentGeneration.Chromosomes[0].GetGenes()));
ga.GenerationRan += (s, e) => Console.WriteLine($"Chromosome Sample 2:" + String.Join("", ga.Population.CurrentGeneration.Chromosomes[1].GetGenes()));
ga.GenerationRan += (s, e) => Console.WriteLine($"Chromosome Sample 3:" + String.Join("", ga.Population.CurrentGeneration.Chromosomes[2].GetGenes()));
ga.GenerationRan += (s, e) => Console.WriteLine($"Chromosome Sample 4:" + String.Join("", ga.Population.CurrentGeneration.Chromosomes[3].GetGenes()));
ga.GenerationRan += (s, e) => Console.WriteLine($"Chromosome Sample 5:" + String.Join("", ga.Population.CurrentGeneration.Chromosomes[4].GetGenes()));
ga.GenerationRan += (s, e) => Console.WriteLine($"Best Chromosome: " + String.Join("", ga.Population.CurrentGeneration.BestChromosome.GetGenes()));
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine();
Console.WriteLine($"Best solution found has fitness: {ga.BestChromosome.Fitness}");
Console.WriteLine($"Elapsed time: {ga.TimeEvolving}");
//Console.ReadKey();
}
public static GeneticSharp.Domain.GeneticAlgorithm DefaultGeneticAlgorithm(Func <double[], double> func, Tuple <double, double>[] minmax)
{
var population = new Population(20, 40, chromosome.CreateIntChromosone(minmax));
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
return(func(values));
});
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var ga = new GeneticSharp.Domain.GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
var termination = new FitnessStagnationTermination(100);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
return(ga);
}
/// <summary>
/// GeneticSharp Console Application template.
/// <see href="https://github.com/giacomelli/GeneticSharp"/>
/// </summary>
static void Main(string[] args)
{
// TODO: use the best genetic algorithm operators to your optimization problem.
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var fitness = new MyProblemFitness();
var chromosome = new MyProblemChromosome();
var population = new Population(50, 70, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new FitnessStagnationTermination(100);
ga.GenerationRan += (s, e) => Console.WriteLine($"Generation {ga.GenerationsNumber}. Best fitness: {ga.BestChromosome.Fitness.Value}");
Console.WriteLine("GA running...");
ga.Start();
Console.WriteLine();
Console.WriteLine($"Best solution found has fitness: {ga.BestChromosome.Fitness}");
Console.WriteLine($"Elapsed time: {ga.TimeEvolving}");
Console.ReadKey();
}
public void Solve()
{
SudokuBoard sudokuBoard = Transform(Sudoku);
IChromosome chromosome = new SudokuPermutationsChromosome(sudokuBoard);
var fitness = new SudokuFitness(sudokuBoard);
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation();
var population = new Population(5000, 5000, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new OrTermination(new ITermination[]
{
new FitnessThresholdTermination(0),
new GenerationNumberTermination(100)
})
};
ga.Start();
var bestIndividual = ((ISudokuChromosome)ga.Population.BestChromosome);
var solutions = bestIndividual.GetSudokus();
Sudoku = Transform(solutions[0]);
}
static async Task Main(string[] args)
{
Console.WriteLine($"[{DateTime.Now}] Tuner start");
SettingsLoader.Init("settings.json");
IFitness fitness = null;
IChromosome chromosome = null;
_webService = new WebService();
await _webService.EnableIfAvailable();
_testId = await _webService.RegisterTest(new RegisterTestRequest
{
Type = TestType.Texel
});
var scalingFactorMode = args.Contains("scaling_factor");
if (scalingFactorMode)
{
SettingsLoader.Data.Genes.Clear();
SettingsLoader.Data.Genes.Add(new GeneInfo
{
Name = "ScalingFactor",
MinValue = 0,
MaxValue = 2000
});
SettingsLoader.Data.Genes.Add(new GeneInfo
{
Name = "Unused",
MinValue = 0,
MaxValue = 1
});
fitness = new ScalingFactorFitness(_testId, _webService);
chromosome = new ScalingFactorChromosome();
}
else
{
fitness = new EvaluationFitness(_testId, _webService);
chromosome = new EvaluationChromosome();
}
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(true);
var population = new Population(SettingsLoader.Data.MinPopulation, SettingsLoader.Data.MaxPopulation, chromosome);
var geneticAlgorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
geneticAlgorithm.Termination = new GenerationNumberTermination(SettingsLoader.Data.GenerationsCount);
geneticAlgorithm.GenerationRan += GeneticAlgorithm_GenerationRan;
_generationStopwatch = new Stopwatch();
_generationStopwatch.Start();
geneticAlgorithm.Start();
Console.WriteLine("Best solution found has {0} fitness.", geneticAlgorithm.BestChromosome.Fitness);
Console.ReadLine();
}
public void OptimizeAI()
{
if (frequency != turnsSinceLastOptimization)
{
turnsSinceLastOptimization++;
return;
}
GenerateScoreData();
int maxSize = GetMaxSize();
AiSettingsChromosome intelligenceChromosome = new AiSettingsChromosome(3);
Population population = new Population(maxSize, maxSize, intelligenceChromosome);
FuncFitness fitness;
if (HistoryManagr.Instance.savingHistory == true)
{
fitness = new FuncFitness((c) => { return(GetHistoryFitness(c)); });
}
else
{
fitness = new FuncFitness((c) => { return(GetFitness(c)); });
}
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new DisplacementMutation();
var termination = new GenerationNumberTermination(2);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
List <IChromosome> savedChromosomes = new List <IChromosome>();
ga.Termination = termination;
ga.GenerationRan += (sender, e) =>
{
if (ga.GenerationsNumber == 2)
{
savedChromosomes = (List <IChromosome>)ga.Population.CurrentGeneration.Chromosomes;
}
};
ga.Start();
turnsSinceLastOptimization = 0;
settingsScoreData.Clear();
settingsData.Clear();
SetGeneratedData(savedChromosomes);
foreach (PlayerData player in PlayerManager.Instance.players)
{
EventSaverManager.Instance.SaveDDAEvolutionAIEvent(player.id);
}
}
/// <summary>
/// Рассчитать топологию для графа проекта с помощью генетического алгоритма.
/// </summary>
/// <param name="project">Объект свойств проекта по внедрению сети.</param>
/// <returns>Сгенерированная топология или null, если произошла ошибка.</returns>
public static Topology CalculateTopologyWithGA(Project project)
{
try
{
Console.Write("Setup GA... ");
var chromosome = new TopologyChromosome(project);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new UniformMutation(true);
var fitness = new TopologyFitness();
var population = new Population(POPULATION_SIZE, POPULATION_SIZE, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(NUMBER_OF_GENERATIONS)
};
// Записать значения в csv файл и строить график фитнес-функции
using (var streamwriter = File.AppendText(Path.Combine(OUTPUT_DIR_NAME, $"fitness-{DateTime.Now.ToString("yyyy-MM-dd-hh-mm-ss")}.csv")))
{
ga.GenerationRan += (c, e) =>
{
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
var bc = ga.Population.BestChromosome as TopologyChromosome;
streamwriter.WriteLine($"{ga.Population.GenerationsNumber};{ga.TimeEvolving:hh\\:mm\\:ss};{bc.Fitness:0.00};");
Console.WriteLine("Best solution found with {0} fitness.", bc.Fitness);
};
Console.Write("Done!\nRun GA... ");
ga.Start();
}
Console.WriteLine("Done in {0} generations!", ga.GenerationsNumber);
var bestChromosome = ga.BestChromosome as TopologyChromosome;
Console.WriteLine("Best solution found with {0} fitness", bestChromosome.Fitness);
return(bestChromosome.Decode());
}
catch (Exception ex)
{
Console.WriteLine("CalculateResultWithGA failed! {0}", ex.Message);
return(null);
}
}
public void PerformTest(int childrenCount, int parentsCount, int cutPoints, int bitAlign)
{
_uniformCrossover = new UniformCrossover()
{
BitAlign = 1, ChildrenCount = childrenCount, ParentsCount = parentsCount
};
var children = _uniformCrossover.Cross(_parents.Take(parentsCount).ToArray());
Assert.NotNull(children);
Assert.AreEqual(childrenCount, children.Length);
foreach (var genotype in children)
{
Assert.NotNull(genotype);
}
Assert.Pass();
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation(true);
var chromosome = new GhostwriterChromosome(4, new string[] { "The", "C#", "Genetic", "Algorithm", "library" });
var fitness = new GhostwriterFitness((t) => t.Length);
var population = new Population(10, 10, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new GenerationNumberTermination(5);
ga.Start();
Assert.NotNull(ga.BestChromosome);
}
public void Start_FloatingPoingChromosome_Evolved()
{
var chromosome = new FloatingPointChromosome(
new double[] { 0, 0, 0, 0 },
new double[] { 1000, 1000, 1000, 1000 },
new int[] { 10, 10, 10, 10 },
new int[] { 0, 0, 0, 0 });
var population = new Population(25, 25, chromosome);
var fitness = new FuncFitness((c) =>
{
var f = c as FloatingPointChromosome;
var values = f.ToFloatingPoints();
var x1 = values[0];
var y1 = values[1];
var x2 = values[2];
var y2 = values[3];
// Euclidean distance: https://en.wikipedia.org/wiki/Euclidean_distance
return(Math.Sqrt(Math.Pow(x2 - x1, 2) + Math.Pow(y2 - y1, 2)));
});
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.Start();
var bc = ga.BestChromosome as FloatingPointChromosome;
var points = bc.ToFloatingPoints();
Assert.AreEqual(4, points.Length);
Assert.AreEqual(1414.2135623730951, bc.Fitness);
Assert.GreaterOrEqual(ga.GenerationsNumber, 100);
}
public static void Main(string[] args)
{
FitnessFunction.Rides = ParseData(out int fleetSize, out int bonus, out int steps);
Chromosome.FleetSize = fleetSize;
FitnessFunction.NumberOfSteps = steps;
FitnessFunction.Bonus = bonus;
var mutation = new UniformMutation(MutationProbability);
var selection = new KTournamentSelection(TournamentSize);
var crossover = new UniformCrossover();
var fitnessFunction = new FitnessFunction();
var geneticAlgorithm = new EliminationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize, FitnessFunction.Rides.Count);
var optimum = geneticAlgorithm.FindOptimum();
WriteOutput(optimum);
}
public Sudoku ResoudreSudoku(Sudoku s)
{
var populationSize = 100000;
var sudokuChromosome = new SudokuPermutationsChromosome(s);
var fitness = new SudokuFitness(s);
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new UniformMutation();
var population = new Population(populationSize, populationSize, sudokuChromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new OrTermination(new ITermination[]
{
new FitnessThresholdTermination(0),
new GenerationNumberTermination(80)
})
};
ga.Start();
var bestIndividual = (ISudokuChromosome)(ga.Population.BestChromosome);
var solutions = bestIndividual.GetSudokus();
return(solutions[0]);
}
public void Evolve_CompareToSingleChromosome_Evolved()
{
int numberOfCities = 30;
var selection = new EliteSelection();
var crossover = new UniformCrossover();
var mutation = new TworsMutation();
// Given enough generations, the Multiple Chromosome should start exhibiting convergence
// we compare TSP /25 gen with 3*TSP / 500 gen
IChromosome chromosome = new TspChromosome(numberOfCities);
IFitness fitness = new TspFitness(numberOfCities, 0, 1000, 0, 1000);
var population = new Population(30, 30, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(26)
};
ga.Start();
var simpleChromosomeDistance = ((TspChromosome)ga.Population.BestChromosome).Distance;
chromosome = new MultipleChromosome((i) => new TspChromosome(numberOfCities), 3);
//MultiChromosome should create 3 TSP chromosomes and store them in the corresponding property
Assert.AreEqual(((MultipleChromosome)chromosome).Chromosomes.Count, 3);
var tempMultiFitness = ((MultipleChromosome)chromosome).Chromosomes.Sum(c => fitness.Evaluate(c));
fitness = new MultipleFitness(fitness);
//Multi fitness should sum over the fitnesses of individual chromosomes
Assert.AreEqual(tempMultiFitness, fitness.Evaluate(chromosome));
population = new Population(30, 30, chromosome);
ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new GenerationNumberTermination(501)
};
ga.Start();
var bestTSPChromosome = (TspChromosome)((MultipleChromosome)ga.Population.BestChromosome).Chromosomes
.OrderByDescending(c => c.Fitness).First();
var multiChromosomesDistance = bestTSPChromosome.Distance;
Assert.Less(multiChromosomesDistance, simpleChromosomeDistance);
}
public static Sudoku Eval(Sudoku sudoku, int populationSize, double fitnessThreshold, int generationNb)
{
//creation du chromosome
IChromosome chromosome = new SudokuPermutationsChromosome(sudoku);
//variable qui indique l'erreure
var fitness = new SudokuFitness(sudoku);
//choix de la selection : ici elite
var selection = new EliteSelection();
//var selection = new RouletteWheelSelection();
//var selection = new SelectionException();
//var selection = new TournamentSelection();
//Choix du crossover : ici uniform
var crossover = new UniformCrossover();
//choix de la mutation : ici uniform
var mutation = new UniformMutation();
//creation de la population
var population = new Population(populationSize, populationSize, chromosome);
//création de l'algo génétique
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
//pour mettre fin a l'exec si on atteint le threshold ou le nombre de rep fixé
Termination = new OrTermination(new ITermination[]
{
new FitnessThresholdTermination(fitnessThreshold),
new GenerationNumberTermination(generationNb)
})
};
ga.Start();
//recupération de la meilleure solution
var bestIndividual = ((ISudokuChromosome)ga.Population.BestChromosome);
var solutions = bestIndividual.GetSudokus();
return(solutions[0]);
}
public Task <Timetable> RunAsync()
{
var adamChromosome = CreateAdamChromosome();
var population = new Population(50, 100, adamChromosome);
var fitness = new FuncFitness(TimetablerFitnessFunction);
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(20));
var geneticAlgorithm = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation)
{
Termination = termination
};
geneticAlgorithm.GenerationRan += (sender, e) =>
{
var bestChromosome = geneticAlgorithm.BestChromosome as FloatingPointChromosome;
GeneticSolution = ChromosomeToTimetable(bestChromosome);
};
GeneticAlgorithm = geneticAlgorithm;
return(Task.Factory.StartNew(() =>
{
geneticAlgorithm.Start();
return GeneticSolution;
}));
}
public static GeneticAlgorithm GetGeneticAlgorithm()
{
var chromosome = new ConfigurationChromosome();
var population = new Population(50, 100, chromosome);
var fitness = new CorrectPercentageFitness();
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new FitnessStagnationTermination(100);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation)
{
Termination = termination
};
return(ga);
}
public void SinglePointCrossoverApplyTest()
{
TestRandom random = new TestRandom();
BinaryVector parent1, parent2, expected, actual;
bool exceptionFired;
// The following test is based on Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg, p. 49
random.Reset();
random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
expected = new BinaryVector(new bool[] { false, true, false, false, false, false, false, false, false });
actual = UniformCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
// The following test is based on Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg, p. 49
random.Reset();
random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
parent2 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
parent1 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
expected = new BinaryVector(new bool[] { true, false, false, true, true, false, false, false, true });
actual = UniformCrossover.Apply(random, parent1, parent2);
Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
// The following test is not based on any published examples
random.Reset();
random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
parent1 = new BinaryVector(new bool[] { false, true, true, false, false }); // this parent is longer
parent2 = new BinaryVector(new bool[] { false, true, true, false });
exceptionFired = false;
try {
actual = UniformCrossover.Apply(random, parent1, parent2);
}
catch (System.ArgumentException) {
exceptionFired = true;
}
Assert.IsTrue(exceptionFired);
}
public static FloatingPointChromosome EvolveGeneticAlgorithm(FloatingPointChromosome chromosome, IOthelloAgent agent, string chromosomeLabel = "")
{
IPopulation population = new TplPopulation(30, 60, chromosome);
IFitness fitness = new EvaluationFitness(agent);
ISelection selection = new RouletteWheelSelection(); //Guess
ICrossover crossover = new UniformCrossover(); //Guess
IMutation mutation = new UniformMutation(); //Guess
ITermination stagnation = new FitnessStagnationTermination(500);
ITermination threshold = new FitnessThresholdTermination(.9);
ITaskExecutor taskExecutor = new ParallelTaskExecutor()
{
MaxThreads = Environment.ProcessorCount,
MinThreads = Environment.ProcessorCount / 2
};
GeneticAlgorithm algorithm = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
TaskExecutor = new TplTaskExecutor(),
MutationProbability = .2f
};
algorithm.TaskExecutor = taskExecutor;
algorithm.Termination = stagnation;
algorithm.Start();
SaveChromosome((FloatingPointChromosome)algorithm.BestChromosome, chromosomeLabel);
Debug.WriteLine("finished Training, with {0} time spent on evolving", algorithm.TimeEvolving);
Debug.WriteLine("fitness of this generation vs the last : {0}", algorithm.Fitness);
return((FloatingPointChromosome)algorithm.BestChromosome);
}