public void SetGens(IntChromosome unit, int unitGensCount)
{
unit.Gens = new List <int>(unitGensCount);
for (int ind = 0; ind < unitGensCount; ind++)
{
unit.Gens.Add(GetIntInRange());
}
}
private IntChromosome FindBest(List <IntChromosome> chromosomes, FitnessFuncGoal goal)
{
IntChromosome bestChromosome = null;
foreach (int index in chromosomeIndexes)
{
if (bestChromosome == null || comparator.Compare(bestChromosome, chromosomes.ElementAt(index)) > 0)
{
bestChromosome = chromosomes.ElementAt(index);
}
}
return(bestChromosome);
}
protected override GeneticAlgorithm GetGA(SolverParameters parameters)
{
var adamChromosome = new IntChromosome(0.To(NumQueens - 1).ToArray());
var population =
new Population(parameters.Population, parameters.Population, adamChromosome);
return(new GeneticAlgorithm(
population,
FitnessProvider,
new EliteSelection(),
new OrderedCrossover(),
new ReverseSequenceMutation()));
}
protected override GeneticAlgorithm GetGA(SolverParameters parameters)
{
// First node is fixed
var adamChromosome = new IntChromosome(1.To(NumPoints - 1).ToArray());
var population =
new Population(parameters.Population, parameters.Population, adamChromosome);
return(new GeneticAlgorithm(
population,
FitnessProvider,
new TournamentSelection(),
new OrderedCrossover(),
new ReverseSequenceMutation()));
}
private void crossbreeding(IntChromosome ch1, IntChromosome ch2)
{
int sharedDischargeBit = getSharedDischarge();
IntChromosome ch1Child = (IntChromosome)ch1.Clone();
IntChromosome ch2Child = (IntChromosome)ch2.Clone();
List <int> ch1ChildGens = ch1Child.Gens;
List <int> ch2ChildGens = ch2Child.Gens;
for (int index = 0; index < ch1ChildGens.Count; index++)
{
ch1ChildGens[index] = CrossbreedingGen(ch1ChildGens.ElementAt(index), ch2ChildGens.ElementAt(index), sharedDischargeBit);
ch2ChildGens[index] = CrossbreedingGen(ch2ChildGens.ElementAt(index), ch1ChildGens.ElementAt(index), sharedDischargeBit);
}
ch1 = ch1Child;
ch2 = ch2Child;
}
private void Mutate(IntChromosome chromosome)
{
int genInd = random.Next(0, chromosome.Gens.Count);
int bitInd = random.Next(0, mutateMask);
if ((chromosome.Gens[genInd] >> (bitInd)) % 2 == 0)
{
//bit equal zero
chromosome.Gens[genInd] += 1 << bitInd;
}
else
{
//bit equal one
chromosome.Gens[genInd] -= 1 << bitInd;
}
}
public List <IntChromosome> Generate(IntRange range, int populationSize, int unitGensCount)
{
if (populationSize < 2 || unitGensCount < 1)
{
throw new ArgumentException("Population must be 2 or more, gens 1 or more");
}
Range = range;
List <IntChromosome> population = new List <IntChromosome>(populationSize);
IntChromosome unit;
for (int ind = 0; ind < populationSize; ind++)
{
unit = new IntChromosome();
SetGens(unit, unitGensCount);
population.Add(unit);
}
return(population);
}
public double CalcFitnessFunc(IntChromosome chromosome)
{
result = 0;
chromosome.Gens.AsParallel().ForAll((gen) => result += Math.Pow(gen, 2));
return(result);
}
