public override List <Chromosome> Run(List <Chromosome> chromosomes, Parameters parameters)
{
chromosomes.Sort();
List <float> distribution = new List <float>(chromosomes.Count);
float partialSum = 0.0f;
var chosenOnes = new List <Chromosome>();
for (int i = 0; i < chromosomes.Count; ++i)
{
switch (parameters.FitnessStrategy)
{
case FitnessStrategy.MINIMIZE:
partialSum += 1.0f / parameters.Fitness.Get(chromosomes[i]);
break;
case FitnessStrategy.MAXIMIZE:
partialSum += parameters.Fitness.Get(chromosomes[i]);
break;
}
distribution.Add(partialSum);
}
for (int i = 0; i < chromosomes.Count; ++i)
{
float nextRand = (float)RandomGeneratorThreadSafe.NextDouble(partialSum);
float chosenOne = distribution.First(d => d >= nextRand);
int index = distribution.IndexOf(chosenOne);
Chromosome chosenSolution = chromosomes[index];
chosenOnes.Add(chosenSolution);
}
return(chosenOnes);
}
/// <summary>
/// Invokes the crossover operations on the next generation.
/// </summary>
/// <param name="population">The population.</param>
/// <param name="parameters">The parameters.</param>
/// <returns></returns>
protected Population Crossover(IProblem problem, Population population, Parameters parameters)
{
Population newPopulation = population, childPopulation;
foreach (var crossover in parameters.CrossoverOperators)
{
List <Thread> crossoverThreads = new List <Thread>();
int threadNumber = Environment.ProcessorCount;
int tasksPerThread = (population.Size / threadNumber);
tasksPerThread += tasksPerThread % 2;
newPopulation = Selection(newPopulation, parameters);
newPopulation.Randomize();
childPopulation = new Population(population.Size);
for (int i = 0; i < threadNumber; ++i)
{
int start = i * tasksPerThread;
int end;
if (i == threadNumber - 1)
{
end = population.Size;
}
else
{
end = (i + 1) * tasksPerThread;
}
Thread thread = new Thread(() => {
Chromosome parent1, parent2, child1, child2;
IGene[] child1Genes, child2Genes;
double nextCrossoverProb;
for (int j = start; j < end; j += 2)
{
nextCrossoverProb = RandomGeneratorThreadSafe.NextDouble();
parent1 = newPopulation[j];
parent2 = newPopulation[j + 1];
if (nextCrossoverProb > parameters.CrossoverProbability)
{
childPopulation[j] = parent1;
childPopulation[j + 1] = parent2;
continue;
}
crossover.Run(parent1.Genes, parent2.Genes, out child1Genes, out child2Genes);
child1 = chromosomeFactory.MakeChromosome(problem, parent1.Genes);
child2 = chromosomeFactory.MakeChromosome(problem, parent2.Genes);
childPopulation[j] = child1;
childPopulation[j + 1] = child2;
}
});
crossoverThreads.Add(thread);
thread.Start();
}
foreach (Thread thread in crossoverThreads)
{
thread.Join();
}
newPopulation = childPopulation;
}
return(newPopulation);
}
protected void AnnealingStep(ref Chromosome currentSolution, ref Chromosome bestSolution, double temperature)
{
Chromosome newSolution = Parameters.ChromosomeFactory.RandomNeighbourSolution(currentSolution, Parameters.MutationOperators.First());
float delta = GetDelta(newSolution, currentSolution);
if (delta < 0)
{
SetNewSolutions(newSolution, ref currentSolution, ref bestSolution);
}
else
{
double x = RandomGeneratorThreadSafe.NextDouble();
double e = Math.Exp(-delta / temperature);
if (x < e)
{
SetNewSolutions(newSolution, ref currentSolution, ref bestSolution);
}
}
}
private void PrepareUniform(int geneCount, out int[] ones, out int[] zeros)
{
List <int> onesList = new List <int>(), zerosList = new List <int>();
double nextDouble;
for (int i = 0; i < geneCount; ++i)
{
nextDouble = RandomGeneratorThreadSafe.NextDouble();
if (nextDouble > 0.50000000)
{
onesList.Add(i);
}
else
{
zerosList.Add(i);
}
}
ones = onesList.ToArray();
zeros = zerosList.ToArray();
}
/// <summary>
/// Invokes the mutation operations on the next generation.
/// </summary>
/// <param name="population">The population.</param>
/// <param name="parameters">The parameters.</param>
/// <returns></returns>
protected void Mutation(Population population, Parameters parameters)
{
foreach (var mutation in parameters.MutationOperators)
{
List <Thread> mutationThreads = new List <Thread>();
int threadNumber = Environment.ProcessorCount;
int tasksPerThread = population.Size / threadNumber;
for (int i = 0; i < threadNumber; ++i)
{
int start = i * tasksPerThread;
int end = (i + 1) * tasksPerThread;
if (i == threadNumber - 1)
{
end += population.Size % threadNumber;
}
Thread thread = new Thread(() => {
double nextMutationProb;
for (int j = start; j < end; ++j)
{
nextMutationProb = RandomGeneratorThreadSafe.NextDouble();
if (nextMutationProb > parameters.MutationProbability)
{
continue;
}
Chromosome chromosome = population[j];
mutation.Run(ref chromosome);
}
});
mutationThreads.Add(thread);
thread.Start();
}
foreach (Thread thread in mutationThreads)
{
thread.Join();
}
}
}
