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 Resume_TerminationReachedAndTerminationExtend_Resumed()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub()
{
SupportsParallel = false
}, selection, crossover, mutation);
target.Population.GenerationStrategy = new TrackingGenerationStrategy();
target.Termination = new GenerationNumberTermination(100);
target.Start();
Assert.AreEqual(100, target.Population.Generations.Count);
var timeEvolving = target.TimeEvolving.Ticks;
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
target.Termination = new GenerationNumberTermination(200);
target.Resume();
Assert.AreEqual(target.Population.Generations.Count, 200);
Assert.Less(timeEvolving, target.TimeEvolving.Ticks);
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
target.Termination = new GenerationNumberTermination(300);
target.Resume();
Assert.AreEqual(target.Population.Generations.Count, 300);
Assert.Less(timeEvolving, target.TimeEvolving.Ticks);
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
}
public void Mutate_AllGenesMutablesTrue_AllGenesMutaed()
{
var target = new UniformMutation(true);
var chromosome = MockRepository.GenerateStub <ChromosomeBase>(3);
chromosome.ReplaceGenes(0, new Gene[]
{
new Gene(1),
new Gene(1),
new Gene(1)
});
chromosome.Expect(c => c.GenerateGene(0)).Return(new Gene(0));
chromosome.Expect(c => c.GenerateGene(1)).Return(new Gene(10));
chromosome.Expect(c => c.GenerateGene(2)).Return(new Gene(20));
RandomizationProvider.Current = MockRepository.GenerateMock <IRandomization>();
target.Mutate(chromosome, 1);
Assert.AreEqual(0, chromosome.GetGene(0).Value);
Assert.AreEqual(10, chromosome.GetGene(1).Value);
Assert.AreEqual(20, chromosome.GetGene(2).Value);
chromosome.VerifyAllExpectations();
RandomizationProvider.Current.VerifyAllExpectations();
}
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 Resume_Stopped_Resumed()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub()
{
SupportsParallel = false
}, selection, crossover, mutation);
target.Termination = new TimeEvolvingTermination(TimeSpan.FromMilliseconds(1000));
Parallel.Invoke(
() => target.Start(),
() =>
{
Thread.Sleep(30);
target.Stop();
Thread.Sleep(30);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
Assert.IsFalse(target.IsRunning);
});
Parallel.Invoke(
() => target.Resume(),
() =>
{
Thread.Sleep(30);
Assert.AreEqual(GeneticAlgorithmState.Resumed, target.State);
Assert.IsTrue(target.IsRunning);
});
}
public void Start_InvalidFitnessEvaluateResult_Exception()
{
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var fitness = MockRepository.GenerateMock <IFitness>();
fitness.Expect(f => f.Evaluate(null)).IgnoreArguments().Return(1.1);
var target = new GeneticAlgorithm(
new Population(20, 20, chromosome),
fitness, selection, crossover, mutation);
ExceptionAssert.IsThrowing(new FitnessException(fitness, "The {0}.Evaluate returns a fitness with value 1.1. The fitness value should be between 0.0 and 1.0.".With(fitness.GetType())), () => {
target.Start();
});
fitness = MockRepository.GenerateMock <IFitness>();
fitness.Expect(f => f.Evaluate(null)).IgnoreArguments().Return(-0.1);
target = new GeneticAlgorithm(
new Population(20, 20, chromosome),
fitness, selection, crossover, mutation);
ExceptionAssert.IsThrowing(new FitnessException(fitness, "The {0}.Evaluate returns a fitness with value -0.1. The fitness value should be between 0.0 and 1.0.".With(fitness.GetType())), () =>
{
target.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 void Main(string[] args)
{
if (args.Length < 1)
{
Console.Error.WriteLine("You have to at least specify the data file path");
return;
}
var mutation = new UniformMutation(MutationProbability);
var selection = new KTournamentSelection(TournamentSize);
var crossover = new ArithmeticCrossover();
var fitnessFunction = new FitnessFunction(args[0]);
IGeneticAlgorithm <DecimalArrayChromosome> geneticAlgorithm;
if (args.Length == 2 && args[1].ToLower() == "gen")
{
geneticAlgorithm = new GenerationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize);
}
else
{
geneticAlgorithm = new EliminationGeneticAlgorithm(mutation, selection, crossover, fitnessFunction,
IterationLimit, FitnessTerminator, PopulationSize);
}
var optimum = geneticAlgorithm.FindOptimum();
Console.WriteLine();
Console.WriteLine(optimum);
}
/// <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 void Resume_TerminationReachedAndTerminationNotChanged_Exception()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub()
{
SupportsParallel = false
}, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(10);
target.Start();
Assert.AreEqual(10, target.Population.Generations.Count);
var timeEvolving = target.TimeEvolving.Ticks;
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.Catch <InvalidOperationException>(() =>
{
target.Resume();
}, "Attempt to resume a genetic algorithm with a termination (GenerationNumberTermination (HasReached: True)) already reached. Please, specify a new termination or extend the current one.");
}
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);
}
public void Start_TplManySlowFitness_Timeout()
{
var taskExecutor = new TplTaskExecutor();
taskExecutor.Timeout = TimeSpan.FromMilliseconds(1000);
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new TplPopulation(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true, ParallelSleep = 1500
}, selection, crossover, mutation);
target.OperatorsStrategy = new TplOperatorsStrategy();
target.TaskExecutor = taskExecutor;
Assert.Catch <TimeoutException>(() =>
{
target.Start();
}, "The fitness evaluation reached the 00:00:01 timeout.");
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
}
public void Start_UsingAllConfigurationCombinationsAvailable_AllRun()
{
var selections = SelectionService.GetSelectionNames();
var crossovers = CrossoverService.GetCrossoverNames();
var mutations = MutationService.GetMutationNames().Where(m => !m.Equals("Flip Bit"));
var reinsertions = ReinsertionService.GetReinsertionNames();
var chromosome = new OrderedChromosomeStub();
foreach (var s in selections)
{
foreach (var c in crossovers)
{
foreach (var m in mutations)
{
foreach (var r in reinsertions)
{
var selection = SelectionService.CreateSelectionByName(s);
var crossover = CrossoverService.CreateCrossoverByName(c);
var mutation = MutationService.CreateMutationByName(m);
var reinsertion = ReinsertionService.CreateReinsertionByName(r);
if (crossover.IsOrdered ^ mutation.IsOrdered)
{
continue;
}
if (crossover.ParentsNumber > crossover.ChildrenNumber && !reinsertion.CanExpand)
{
continue;
}
if (mutation is UniformMutation)
{
mutation = new UniformMutation(1);
}
var target = new GeneticAlgorithm(
new Population(50, 50, chromosome.Clone())
{
GenerationStrategy = new TrackingGenerationStrategy()
},
new FitnessStub()
{
SupportsParallel = false
},
selection,
crossover,
mutation);
target.Reinsertion = reinsertion;
target.Termination = new GenerationNumberTermination(25);
target.CrossoverProbability = reinsertion.CanExpand ? 0.75f : 1f;
target.Start();
Assert.AreEqual(25, target.Population.Generations.Count);
}
}
}
}
}
public void Start_ParallelManySlowFitness_Timeout()
{
var taskExecutor = new SmartThreadPoolTaskExecutor();
taskExecutor.MinThreads = 100;
taskExecutor.MaxThreads = 100;
taskExecutor.Timeout = TimeSpan.FromMilliseconds(1000);
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true, ParallelSleep = 1500
}, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
ExceptionAssert.IsThrowing(new TimeoutException("The fitness evaluation rech the 00:00:01 timeout."), () =>
{
target.Start();
});
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
}
public void Stop_Started_Stopped()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub()
{
SupportsParallel = false
}, selection, crossover, mutation);
target.Termination = new GenerationNumberTermination(10000);
Parallel.Invoke(
() => target.Start(),
() =>
{
Thread.Sleep(10);
Assert.AreEqual(GeneticAlgorithmState.Started, target.State);
Assert.IsTrue(target.IsRunning);
target.Stop();
Thread.Sleep(30);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
Assert.IsFalse(target.IsRunning);
});
Assert.Less(target.Population.Generations.Count, 10000);
Assert.Less(target.TimeEvolving.TotalMilliseconds, 1000);
}
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);
}
/// <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]);
}
public void Evolve_ManyGenerations_Fast()
{
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = new FunctionBuilderFitness(
new FunctionBuilderInput(
new double[] { 1, 2 },
3)
,
new FunctionBuilderInput(
new double[] { 2, 3 },
5)
);
var chromosome = new FunctionBuilderChromosome(fitness.AvailableOperations, 3);
var population = new Population(100, 200, chromosome);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ga.Termination = new OrTermination(new FitnessThresholdTermination(0), new TimeEvolvingTermination(TimeSpan.FromSeconds(15)));
ga.Start();
var bestChromosome = ga.BestChromosome as FunctionBuilderChromosome;
Assert.AreEqual(0.0, bestChromosome.Fitness.Value);
var actual = fitness.GetFunctionResult(
bestChromosome.BuildFunction(),
new FunctionBuilderInput(new double[] { 3, 4 }, 7)
);
Assert.AreEqual(7, actual);
}
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 void Start_TerminationReached_TerminationReachedEventRaised()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var target = new GeneticAlgorithm(new Population(100, 199, chromosome),
new FitnessStub()
{
SupportsParallel = false
}, selection, crossover, mutation);
target.Population.GenerationStrategy = new TrackingGenerationStrategy();
target.Termination = new GenerationNumberTermination(1);
var raised = false;
target.TerminationReached += (e, a) =>
{
raised = true;
};
target.Start();
Assert.IsTrue(raised);
}
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();
}
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 IMutation UniformMutation()
{
var target = new UniformMutation();
target.Mutate(new TspChromosome(_numberOfCities), _probability);
return(target);
}
public void Should_Perform_Mutation()
{
var algorithm = new UniformMutation(1, -0.1, 0.1);
var a = new Element(new double[] { 8D, 3D, 2D, 9D, 1D, 0D, 7D, 6D, 5D, 4D });
var index = 1;
algorithm.Mutate(ref a, index);
Assert.NotEqual(3D, a.Data[1]);
Assert.InRange(a.Data[1], 2.9, 3.1);
}
/// <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 Start_ParallelManyGenerations_Optimization()
{
var taskExecutor = new ParallelTaskExecutor();
taskExecutor.MinThreads = 100;
taskExecutor.MaxThreads = 100;
var selection = new EliteSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
FlowAssert.IsAtLeastOneAttemptOk(20, () =>
{
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true
}, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
Assert.AreEqual(GeneticAlgorithmState.NotStarted, target.State);
Assert.IsFalse(target.IsRunning);
target.Start();
Assert.AreEqual(GeneticAlgorithmState.TerminationReached, target.State);
Assert.IsFalse(target.IsRunning);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count >= 100);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count <= 150);
Assert.IsNotNull(target.Population.BestChromosome);
Assert.IsTrue(target.Population.BestChromosome.Fitness >= 0.9);
Assert.IsTrue(target.Population.Generations.Count > 0);
});
FlowAssert.IsAtLeastOneAttemptOk(20, () =>
{
var target = new GeneticAlgorithm(new Population(100, 150, chromosome),
new FitnessStub()
{
SupportsParallel = true
}, selection, crossover, mutation);
target.TaskExecutor = taskExecutor;
target.Start();
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count >= 100);
Assert.IsTrue(target.Population.CurrentGeneration.Chromosomes.Count <= 150);
Assert.IsNotNull(target.Population.BestChromosome);
Assert.IsTrue(target.Population.BestChromosome.Fitness >= 0.9);
Assert.IsTrue(target.Population.Generations.Count > 0);
});
}
static void Main(string[] args)
{
// Problem: Simple addition
// 1 + 1 = 2
// 2 + 2 = 4
// 3 + 2 = 5
var myInputs = new List <FunctionBuilderInput>
{
new FunctionBuilderInput(new List <double> {
1, 1
}, 2),
new FunctionBuilderInput(new List <double> {
2, 2
}, 4),
new FunctionBuilderInput(new List <double> {
3, 2
}, 5)
};
var myFitness = new FunctionBuilderFitness(myInputs.ToArray());
var myChromosome = new FunctionBuilderChromosome(myFitness.AvailableOperations, 5);
var selection = new EliteSelection();
var crossover = new ThreeParentCrossover();
var mutation = new UniformMutation(true);
var fitness = myFitness;
var chromosome = myChromosome;
var population = new Population(100, 200, chromosome)
{
GenerationStrategy = new PerformanceGenerationStrategy()
};
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation)
{
Termination = new FitnessThresholdTermination(0)
};
ga.GenerationRan += delegate
{
Console.Clear();
var bestChromosome = ga.Population.BestChromosome;
Console.WriteLine("Generations: {0}", ga.Population.GenerationsNumber);
Console.WriteLine("Fitness: {0,10}", bestChromosome.Fitness);
Console.WriteLine("Time: {0}", ga.TimeEvolving);
Console.WriteLine("Speed (gen/sec): {0:0.0000}", ga.Population.GenerationsNumber / ga.TimeEvolving.TotalSeconds);
var best = bestChromosome as FunctionBuilderChromosome;
Console.WriteLine("Function: {0}", best.BuildFunction());
};
ga.Start();
Console.WriteLine("Evolved.");
Console.ReadKey();
}
public void Start_FitnessEvaluationFailed_FitnessException()
{
var selection = new RouletteWheelSelection();
var crossover = new OnePointCrossover(1);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var fitness = new FuncFitness((c) => throw new Exception("TEST"));
var target = new GeneticAlgorithm(new Population(100, 150, chromosome), fitness, selection, crossover, mutation);
Assert.Catch <FitnessException>(target.Start);
Assert.IsFalse(target.IsRunning);
Assert.AreEqual(GeneticAlgorithmState.Stopped, target.State);
}
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);
}
