protected override GeneticAlgorithm CreateGA()
{
m_fitness = new WallBuilderFitness(SecondsForEvaluation / TimeScale);
var chromosome = new WallBuilderChromosome(BricksCount, MinPosition, MaxPosition);
var crossover = new RandomCrossover()
.AddCrossover(new UniformCrossover(), 0.9f)
.AddCrossover(new SectionCrossover(chromosome.Length / chromosome.BricksCount, true), 0.1f);
var mutation = new RandomMutation()
.AddMutation(new FlipBitMutation(), .9f)
.AddMutation(new UniformMutation(), .1f);
var selection = new EliteSelection();
var population = new Population(NumberOfSimultaneousEvaluations, NumberOfSimultaneousEvaluations, chromosome);
var ga = new GeneticAlgorithm(population, m_fitness, selection, crossover, mutation);
ga.Termination = new TimeEvolvingTermination(System.TimeSpan.FromDays(1));
ga.TaskExecutor = new ParallelTaskExecutor
{
MinThreads = population.MinSize,
MaxThreads = population.MaxSize * 2
};
ga.GenerationRan += delegate
{
m_lastPosition = Vector3.zero;
};
return(ga);
}
public override List <Candidate> Crossover(Candidate parentX, Candidate parentY)
{
List <Candidate> childrenList = new List <Candidate>();
float chance = (float)RandomCrossover.NextDouble();
if (chance < CrossoverChance)
{
IntegrityHelper.checkGens(parentX);
IntegrityHelper.checkGens(parentY);
int startIndex = RandomCrossover.Next(0, parentX.chromoson.Count() - 1);
int endIndex = RandomCrossover.Next(0, parentX.chromoson.Count() - 1); //random indexes
if (startIndex > endIndex)
{
int temp = startIndex;
startIndex = endIndex;
endIndex = temp;
}
int[,] mappingArray = createMappingArray(parentX, parentY, startIndex, endIndex);
int[] childXChromosome = new int[parentX.chromoson.Count()];
int[] childYChromosome = new int[parentX.chromoson.Count()];
FillChromosone(ref childXChromosome, startIndex, mappingArray, 1);
FillChromosone(ref childYChromosome, startIndex, mappingArray, 0);
fillMissingGens(ref childXChromosome, parentX, startIndex, endIndex);
fillMissingGens(ref childYChromosome, parentY, startIndex, endIndex);
fillMappedGens(ref childXChromosome, mappingArray, parentX);
fillDoubleMapp(ref childXChromosome, mappingArray, parentX);
fillMappedGens(ref childYChromosome, mappingArray, parentY);
fillDoubleMapp(ref childYChromosome, mappingArray, parentY);
Candidate childX = new Candidate(parentX.generation, childXChromosome.ToList(), parentX.solver, parentX.solver.time.ElapsedMilliseconds.ToString());
Candidate childY = new Candidate(parentY.generation, childYChromosome.ToList(), parentY.solver, parentY.solver.time.ElapsedMilliseconds.ToString());
childX.generation = (parentX.generation + 1);
childY.generation = (parentY.generation + 1);
childrenList.Add(childX);
childrenList.Add(childY);
IntegrityHelper.checkGens(childrenList);
}
else
{
Candidate childX = new Candidate(parentX.generation, parentX.chromoson, parentX.solver, parentX.solver.time.ElapsedMilliseconds.ToString());
Candidate childY = new Candidate(parentY.generation, parentY.chromoson, parentY.solver, parentY.solver.time.ElapsedMilliseconds.ToString());
childX.generation = (parentX.generation + 1);
childY.generation = (parentY.generation + 1);
childrenList.Add(childX);
childrenList.Add(childY);
IntegrityHelper.checkGens(childrenList);
}
return(childrenList);
}
public override List <Candidate> CrossoverPopulation(List <Candidate> population, int populationSize)
{
int parentX;
int parentY;
List <Candidate> newPopulation = new List <Candidate>();
int size = populationSize / 2;
for (int i = 0; i < size; i++)
{
parentX = RandomCrossover.Next(0, population.Count());
parentY = RandomCrossover.Next(0, population.Count());
newPopulation.AddRange(Crossover(population[parentX], population[parentY]));
}
return(newPopulation);
}
public override List <Candidate> Crossover(Candidate parentX, Candidate parentY)
{
List <Candidate> childrenList = new List <Candidate>();
float chance = (float)RandomCrossover.NextDouble();
if (chance < CrossoverChance)
{
int startIndex = RandomCrossover.Next(0, parentX.chromoson.Count() - 1);
int endIndex = RandomCrossover.Next(0, parentX.chromoson.Count() - 1);
if (startIndex > endIndex)
{
int temp = startIndex;
startIndex = endIndex;
endIndex = temp;
}
int[] childXChromosome = new int[parentX.chromoson.Count()];
int[] childYChromosome = new int[parentX.chromoson.Count()];
int[] parentYSection = new int[endIndex - startIndex];
int[] parentXSection = new int[endIndex - startIndex];
fillParentSection(ref parentXSection, parentX.chromoson, startIndex, endIndex);
fillParentSection(ref parentYSection, parentY.chromoson, startIndex, endIndex);
FillChromosoneWithParenScetion(ref childXChromosome, parentYSection, startIndex);
FillChromosoneWithParenScetion(ref childYChromosome, parentXSection, startIndex);
FillChromosoneWithMissingGens(ref childXChromosome, parentX.chromoson, endIndex);
FillChromosoneWithMissingGens(ref childYChromosome, parentY.chromoson, endIndex);
Candidate childX = new Candidate(parentX.generation + 1, childXChromosome.ToList(), parentX.solver, parentX.solver.time.ElapsedMilliseconds.ToString());
Candidate childY = new Candidate(parentY.generation + 1, childYChromosome.ToList(), parentY.solver, parentY.solver.time.ElapsedMilliseconds.ToString());
childrenList.Add(childX);
childrenList.Add(childY);
IntegrityHelper.checkGens(childrenList);
}
else
{
Candidate childX = new Candidate(parentX.generation, parentX.chromoson, parentX.solver, parentX.solver.time.ElapsedMilliseconds.ToString());
Candidate childY = new Candidate(parentY.generation, parentY.chromoson, parentY.solver, parentY.solver.time.ElapsedMilliseconds.ToString());
childX.generation = (parentX.generation + 1);
childY.generation = (parentY.generation + 1);
childrenList.Add(childX);
childrenList.Add(childY);
IntegrityHelper.checkGens(childrenList);
}
return(childrenList);
}
public override List <Candidate> CrossoverPopulation(List <Candidate> population, int populationSize)
{
int parentX;
int parentY;
ConcurrentQueue <Candidate> newPopulation = new ConcurrentQueue <Candidate>();
int size = populationSize / 2;
var res = Parallel.For(0, size, MultiTaskOptions.parallelOptCrossover, i =>
{
parentX = RandomCrossover.Next(0, population.Count());
parentY = RandomCrossover.Next(0, population.Count());
var children = Crossover(population[parentX], population[parentY]);
newPopulation.Enqueue(children[0]);
newPopulation.Enqueue(children[1]);
});
while (!res.IsCompleted)
{
}
return(newPopulation.ToList());
}
