static void Main(string[] args)
{
List<RealAxis> R = new List<RealAxis>();
List<DiscreteAxis> D = new List<DiscreteAxis>();
List<BinaryAxis> B = new List<BinaryAxis>();
for(int i = 0; i < 3; i++)
{
R.Add(new RealAxis(-32.768, 32.768));
}
Space S = new Space(R, D, B);
Map<Space, Individual, double> M = new Map<Space, Individual, double>(S, false, 0.5, ME2Functions.RandomIndividual,
MapFunctions.MapNeighbors, ME2Functions.Ackley, ME2Functions.ApplyFitness, ME2Functions.RouletteSelection);
Individual Best = M.MappedSpace.ElementAt(0);
foreach(Individual potentialBest in M.MappedSpace)
{
if (potentialBest.Fitness > Best.Fitness)
Best = potentialBest;
}
Console.WriteLine(Best);
Explore<Space, Individual, double> E1 = new Explore<Space, Individual, double>(S, false, M.MappedSpace, ExploitFunctions.ExploitNeighbors,
ME2Functions.NormalMutate, ME2Functions.Ackley, ME2Functions.ApplyFitness, ExploreFunctions.ApplyExplorePOM, ExploreFunctions.ExploreTP,
ME2Functions.TotalFitness, 100);
Best = E1.Optimized.ElementAt(0);
foreach (Individual potentialBest in E1.Optimized)
{
if (potentialBest.Fitness > Best.Fitness)
Best = potentialBest;
}
Console.WriteLine(Best);
Exploit<Space, Individual, double> E2 = new Exploit<Space, Individual, double>(S, false, E1.Optimized, ExploitFunctions.ExploitNeighbors,
ME2Functions.NormalMutate, ME2Functions.Ackley, ME2Functions.ApplyFitness, ExploitFunctions.ApplyExploitPOM, ExploitFunctions.ExploitTP,
ME2Functions.TotalFitness, 100);
Best = E2.Optimized.First();
foreach (Individual potentialBest in E2.Optimized)
{
if (potentialBest.Fitness > Best.Fitness)
Best = potentialBest;
}
Console.WriteLine(Best);
}
public static double NormalizedCityBlockDistance(Space SearchSpace, Individual Source, Individual Destination)
{
double ncbd = 0.0;
for (int i = 0; i < Source.Size; i++)
{
if (Source[i] is bool)
{
ncbd += 1.0;
}
else if (Source[i] is int)
{
ncbd += (double)(Math.Abs((int)Source[i] - (int)Destination[i]) / ((DiscreteAxis)SearchSpace[i]).RangeProper);
}
else if (Source[i] is double)
{
ncbd += (Math.Abs((double)Source[i] - (double)Destination[i]) / ((RealAxis)SearchSpace[i]).RangeProper);
}
}
return ncbd;
}
public static Individual NormalMutate(Space SearchSpace, Individual IndividualToMutate, Func<Individual, bool, double> Fitness, bool maximize, Action<Individual, double> ApplyFitness)
{
// First mutate sigma
double tau = 1.0 / (2.0 * Math.Sqrt(IndividualToMutate.Size)); // Learning rate inversely proportional to the sqrt of genotype size
double NT = Normal.Sample(ME2Functions._rng, 0, tau);
// The value below CANNOT BE USED as sigma
// Sigma is this value * the allele value
IndividualToMutate.MutationStep = IndividualToMutate.MutationStep * Math.Exp(NT);
// Now create a mutant genotype using the original genotype of the Individual
var resultingGenotype = new List<object>();
double probability = 0.0;
for (int i = 0; i < IndividualToMutate.Size; i++)
{
// Check whether to mutate this allele
probability = ContinuousUniform.Sample(ME2Functions._rng, 0.0, 1.0);
if (probability < IndividualToMutate.MutationProbability)
{
// Allele will be mutated
if (IndividualToMutate[i] is bool)
{
resultingGenotype.Add(!(bool)IndividualToMutate[i]);
}
else if (IndividualToMutate[i] is int)
{
double newAllele = (double)IndividualToMutate[i];
double NS = Normal.Sample(ME2Functions._rng, 0, Math.Abs(newAllele * IndividualToMutate.MutationStep));
newAllele += NS;
int minValue = ((DiscreteAxis)SearchSpace[i]).Min;
int maxValue = ((DiscreteAxis)SearchSpace[i]).Max;
if(newAllele < minValue)
{
newAllele = minValue;
}
else if(newAllele > maxValue)
{
newAllele = maxValue;
}
resultingGenotype.Add((int)newAllele);
}
else
{
double newAllele = (double)IndividualToMutate[i];
double NS = Normal.Sample(ME2Functions._rng, 0, Math.Abs(newAllele * IndividualToMutate.MutationStep));
newAllele += NS;
double minValue = ((RealAxis)SearchSpace[i]).Min;
double maxValue = ((RealAxis)SearchSpace[i]).Max;
if(newAllele < minValue)
{
newAllele = minValue;
}
else if(newAllele > maxValue)
{
newAllele = maxValue;
}
resultingGenotype.Add(newAllele);
}
}
else
{
// Allele will not be mutated
resultingGenotype.Add(IndividualToMutate[i]);
}
}
// Return an Individual with the mutant genotype
Individual mutant = new Individual();
mutant.Genotype = resultingGenotype;
mutant.MutationProbability = IndividualToMutate.MutationProbability;
mutant.MutationStep = IndividualToMutate.MutationStep;
double newFitness = Fitness(mutant, maximize);
ApplyFitness(mutant, newFitness);
return mutant;
}
public static Individual RandomIndividual(Space SearchSpace)
{
Individual randomIndividual = new Individual();
for (int i = 0; i < SearchSpace.Size; i++)
{
if (i >= 0 && i < SearchSpace.RealAxes)
{
randomIndividual.Genotype.Add(ContinuousUniform.Sample(ME2Functions._rng, (double)((RealAxis)SearchSpace[i]).Min, (double)((RealAxis)SearchSpace[i]).Max));
}
else if (i >= SearchSpace.RealAxes && i < SearchSpace.DiscreteAxes)
{
randomIndividual.Genotype.Add(DiscreteUniform.Sample(ME2Functions._rng, (int)((DiscreteAxis)SearchSpace[i]).Min, (int)((DiscreteAxis)SearchSpace[i]).Max));
}
else
{
bool randomBool = Convert.ToBoolean(ME2Functions._rng.Next(0, 2));
randomIndividual.Genotype.Add(randomBool);
}
}
randomIndividual.MutationStep = 1.0;
randomIndividual.MutationProbability = 1.0;
return randomIndividual;
}