/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.GeneticAlgorithm"/> class.
/// </summary>
/// <param name="population">The chromosomes population.</param>
/// <param name="fitness">The fitness evaluation function.</param>
/// <param name="selection">The selection operator.</param>
/// <param name="crossover">The crossover operator.</param>
/// <param name="mutation">The mutation operator.</param>
public GeneticAlgorithm(
IPopulation population,
IFitness fitness,
ISelection selection,
ICrossover crossover,
IMutation mutation)
{
ExceptionHelper.ThrowIfNull("population", population);
ExceptionHelper.ThrowIfNull("fitness", fitness);
ExceptionHelper.ThrowIfNull("selection", selection);
ExceptionHelper.ThrowIfNull("crossover", crossover);
ExceptionHelper.ThrowIfNull("mutation", mutation);
Population = population;
Fitness = fitness;
Selection = selection;
Crossover = crossover;
Mutation = mutation;
Reinsertion = new ElitistReinsertion();
Termination = new GenerationNumberTermination(1);
CrossoverProbability = DefaultCrossoverProbability;
MutationProbability = DefaultMutationProbability;
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
TaskExecutor = new LinearTaskExecutor();
}
/// <summary>
/// Initializes a new instance of the <see cref="AutoConfigFitness"/> class.
/// </summary>
/// <param name="targetFitness">The target fitness.</param>
/// <param name="targetChromosome">The target chromosome.</param>
public AutoConfigFitness(IFitness targetFitness, IChromosome targetChromosome)
{
m_targetFitness = targetFitness;
m_targetChromosome = targetChromosome;
PopulationMinSize = 100;
PopulationMaxSize = 100;
Termination = new TimeEvolvingTermination(TimeSpan.FromSeconds(30));
TaskExecutor = new LinearTaskExecutor();
}
public void Start_Task_TaskRan()
{
var pipeline = "";
var target = new LinearTaskExecutor();
target.Add(() => pipeline += "1");
target.Add(() => pipeline += "2");
target.Add(() => pipeline += "3");
Assert.IsTrue(target.Start());
Assert.AreEqual("123", pipeline);
}
public void Start_TakeMoreThanTimeout_False()
{
var pipeline = "";
var target = new LinearTaskExecutor();
target.Add(() => pipeline += "1");
target.Add(() => {
pipeline += "2";
Thread.Sleep(100);
});
target.Add(() => pipeline += "3");
target.Timeout = TimeSpan.FromMilliseconds(50);
Assert.IsFalse(target.Start());
Assert.AreEqual("12", pipeline);
}
public void Stop_ManyTasks_True()
{
var pipeline = "";
var target = new LinearTaskExecutor();
target.Add(() => pipeline += "1");
target.Add(() =>
{
pipeline += "2";
Thread.Sleep(1000);
});
target.Add(() => pipeline += "3");
Parallel.Invoke(
() => Assert.IsTrue(target.Start()),
() =>
{
Thread.Sleep(5);
target.Stop();
});
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.GeneticAlgorithmCCE"/> class.
/// </summary>
/// <param name="species">The species for this CCE</param>
public GeneticAlgorithmCCE(List <CCESpecies> species)
{
Species = species;
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
// TaskExecutorFitMaster = new SmartThreadPoolTaskExecutor () {
// MinThreads = 4,
// MaxThreads = 8
// };
TaskExecutorFitMaster = new LinearTaskExecutor();
TaskExecutorFit = new LinearTaskExecutor();
// TaskExecutorGen = new LinearTaskExecutor ();
// TaskExecutorFit = new SmartThreadPoolTaskExecutor()
// {
// MinThreads = 5,
// MaxThreads = 60
// };
TaskExecutorGen = new SmartThreadPoolTaskExecutor()
{
MinThreads = 4,
MaxThreads = 8
};
}
/// <summary>
/// Initializes a new instance of the <see cref="AlgorithmOptimumFinder"/> class
/// </summary>
/// <param name="start">Algorithm start date</param>
/// <param name="end">Algorithm end date</param>
/// <param name="fitScore">Argument of <see cref="FitnessScore"/> type. Fintess function to rank the backtest results</param>
/// <param name="filterEnabled">Indicates whether to apply fitness filter to backtest results</param>
public AlgorithmOptimumFinder(DateTime start, DateTime end, FitnessScore fitScore, bool filterEnabled)
{
// Assign Dates and Criteria to sort the results
StartDate = start;
EndDate = end;
FitnessScore = fitScore;
// Common properties
var selection = new RouletteWheelSelection();
// Properties specific to optimization modes
IFitness fitness;
PopulationBase population;
ITaskExecutor executor;
ITermination termination;
// Task execution mode
switch (Shared.Config.TaskExecutionMode)
{
// Enable fitness filtering while searching for optimum parameters
case TaskExecutionMode.Linear:
executor = new LinearTaskExecutor();
fitness = new OptimizerFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
case TaskExecutionMode.Parallel:
executor = new ParallelTaskExecutor();
fitness = new OptimizerFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
case TaskExecutionMode.Azure:
executor = new ParallelTaskExecutor();
fitness = new AzureFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
default:
throw new Exception("Executor initialization failed");
}
// Optimization mode
switch (Shared.Config.OptimizationMode)
{
case OptimizationMode.BruteForce:
{
// Create cartesian population
population = new PopulationCartesian(Shared.Config.GeneConfigArray);
termination = new GenerationNumberTermination(1);
break;
}
case OptimizationMode.Genetic:
{
// Create random population
population = new PopulationRandom(Shared.Config.GeneConfigArray, Shared.Config.PopulationInitialSize)
{
GenerationMaxSize = Shared.Config.GenerationMaxSize
};
// Logical terminaton
var localTerm = new LogicalOrTermination();
localTerm.AddTermination(new FruitlessGenerationsTermination(3));
if (Shared.Config.Generations.HasValue)
{
localTerm.AddTermination(new GenerationNumberTermination(Shared.Config.Generations.Value));
}
if (Shared.Config.StagnationGenerations.HasValue)
{
localTerm.AddTermination(new FitnessStagnationTermination(Shared.Config.StagnationGenerations.Value));
}
termination = localTerm;
break;
}
default:
throw new Exception("Optimization mode specific objects were not initialized");
}
// Create GA itself
GenAlgorithm = new GeneticAlgorithm(population, fitness, executor)
{
// Reference types
Selection = selection,
Termination = termination,
// Values types
CrossoverParentsNumber = Shared.Config.CrossoverParentsNumber,
CrossoverMixProbability = Shared.Config.CrossoverMixProbability,
MutationProbability = Shared.Config.MutationProbability
};
}
