/// <summary>
/// Applies a global aggregator (typically sum, max or average) to child chromosomes fitnesses, given the individual fitness function
/// </summary>
/// <param name="childChromosomes">the child chromosomes to be aggregated</param>
/// <param name="individualFitness">the individual fitness function to evaluate child chromosomes</param>
/// <param name="aggregator">the global aggregator for the child fitnesses</param>
/// <returns>an aggregated global fitness</returns>
private static double ApplyAggregator(IEnumerable <IChromosome> childChromosomes, IFitness individualFitness, Func <IEnumerable <IChromosome>, Func <IChromosome, double>, double> aggregator)
{
var chromosomesEnumerated = childChromosomes as IList <IChromosome> ?? childChromosomes.ToList();
foreach (var childChromosome in chromosomesEnumerated)
{
childChromosome.Fitness = individualFitness.Evaluate(childChromosome);
}
return(aggregator(chromosomesEnumerated, individualFitness.Evaluate));
}
/// <summary>
/// Parallel fitness evaluation.
/// </summary>
/// <param name="fitness">Fitness.</param>
/// <param name="population">Input Population</param>
public override void EvaluateFitness(IFitness fitness, IList <IIndividual> population)
{
Parallel.ForEach(population, ind =>
{
if (!ind.Fitness.HasValue)
{
ind.Fitness = fitness.Evaluate(ind);
}
});
// population.Individuals = population.Individuals.OrderByDescending(c => c.Fitness.Value).ToList();
}
protected override void _evaluation()
{
Parallel.ForEach(_population, new ParallelOptions {
MaxDegreeOfParallelism = 8
}, (x) =>
{
if (x.Fitness == null)
{
x.Fitness = _selector.Evaluate(x)[0];
}
});
}
protected override IList <IChromosome> PerformSelectChromosomes(IPopulation population, IList <IChromosome> offspring, IList <IChromosome> parents)
{
var newPopulation = offspring.Concat(parents).ToList();
foreach (var chromosome in newPopulation)
{
if (!chromosome.Fitness.HasValue)
{
chromosome.Fitness = _fitness.Evaluate(chromosome);
}
}
return(newPopulation.OrderByDescending(p => p.Fitness).Take(population.MaxSize).ToList());
}
/// <summary>
/// Linear fitness evaluation.
/// </summary>
/// <param name="fitness">Fitness.</param>
/// <param name="population">Input Population</param>
public virtual void EvaluateFitness(IFitness fitness, IList <IIndividual> population)
{
foreach (var ind in population)
{
//non-fitness value
if (!ind.Fitness.HasValue)
{
// recalculation fitness for cur. individual
ind.Fitness = fitness.Evaluate(ind);
}
}
// sorts population by fitness
// population.Individuals = population.Individuals.OrderByDescending(c => c.Fitness.Value).ToList();
}
private void ShowResult(IChromosome chromosome, IFitness fitness)
{
for (var i = 0; i < 30; i++)
{
Console.Write(fitness.Evaluate(chromosome) + " ");
foreach (var gene in chromosome.GetGenes())
{
var server = (Server)gene.Value;
var serviceNames = server.Services.Select(x => x.Name.ToString()).ToList();
Console.Write("Сервер - " + server.Name + ":{ ");
serviceNames.ForEach(x => Console.Write(x + ";"));
Console.Write("} ");
}
Console.WriteLine();
Console.WriteLine("Next");
}
}
protected override void _evaluation()
{
Parallel.ForEach(_population, new ParallelOptions {
MaxDegreeOfParallelism = 8
}, (x) =>
{
if (x.Fitness == null)
{
//#TODO Add object with Coverage and Fitness
x.Fitness = _selector.Evaluate(x)[0];
Console.WriteLine("Processing {0} on thread {1}", x.Fitness.Value, Thread.CurrentThread.ManagedThreadId);
}
else
{
Console.WriteLine("Already Set!");
}
});
}
protected override void _evaluation()
{
Parallel.ForEach(_population, new ParallelOptions {
MaxDegreeOfParallelism = 8
}, (x) =>
{
if (x.Fitness == null)
{
//This is a good example of using OO to prevent things from happening by using objects and our custom type
//Example of encapsulation - information hiding
//TODO fix reflection in high performance code
x.Fitness = _selector.Evaluate(x)[0];
Console.WriteLine("Returned {0} on thread {1}", x.Fitness, Thread.CurrentThread.ManagedThreadId);
}
else
{
Console.WriteLine("Already Set!");
}
});
}
protected override void _evaluation()
{
Parallel.ForEach(_population, new ParallelOptions {
MaxDegreeOfParallelism = 8
}, (x) =>
{
if (x.Fitness == null)
{
x.Fitness = _selector.Evaluate(x)[0];
#if DEBUG
Console.WriteLine("Processing {0} on thread {1}", x.Fitness.Value, Thread.CurrentThread.ManagedThreadId);
#endif
}
else
{
#if DEBUG
Console.WriteLine("Already Set!");
#endif
}
});
}
protected override IList <IChromosome> PerformSelectChromosomes(IPopulation population,
IList <IChromosome> offspring, IList <IChromosome> parents)
{
// evaluate fitness of all
Parallel.ForEach(offspring.Concat(parents),
new ParallelOptions()
{
MaxDegreeOfParallelism = Global.Config.ThreadsCount
},
x => fitness.Evaluate(x));
IList <IChromosome> resultOffspring;
// if not enough offsprings => take from parents
if (offspring.Count < population.MinSize)
{
var parentsToAdd = selection.SelectChromosomes(population.MinSize - offspring.Count,
new Generation(parents.Count, parents));
resultOffspring = offspring;
foreach (var chromosome in parentsToAdd)
{
resultOffspring.Add(chromosome);
}
}
// if too many offsprings => take right number from them
else if (offspring.Count > population.MinSize)
{
resultOffspring = selection.SelectChromosomes(population.MinSize,
new Generation(offspring.Count, offspring));
}
else
{
resultOffspring = offspring;
}
// perform elitism (take n best samples from parents and add it to offsprings)
resultOffspring = elitism.SelectChromosomes(population, resultOffspring, parents);
return(resultOffspring);
}
public void EvaluateFitness(IFitness <T> function)
{
m_Fitness = function.Evaluate(this);
}
public double[] Evaluate(StubIndividual item)
{
Console.WriteLine(item.ToString());
return(_fitness.Evaluate(item));
}
public void EvaluateFitness(IFitness function)
{
Fitness = function.Evaluate(this);
}
