public override void CalculateFitness(AbstractPhenotype phenotype)
{
var sim = new MinCogSimulator.MinCogSimulator((MinCogPhenotype)phenotype);
sim.Simulate();
double score = sim.GoodHits - sim.BadHits;
phenotype.Fitness = (score > 0) ? score : 0;
}
public override void CalculateFitness(AbstractPhenotype phenotype)
{
var cbp = (ColonelBlottoPhenotype) phenotype;
cbp.Fitness = (cbp.Wins*2) + cbp.Ties;
cbp.Wins = 0;
cbp.Ties = 0;
}
public override List<BitVector> Crossover(AbstractPhenotype parent1, AbstractPhenotype parent2,
double crossoverRate)
{
var par1 = (BitVector) parent1.Genotype;
var par2 = (BitVector) parent2.Genotype;
var offspring = new List<BitVector>();
int randomIndex = _random.Next(1, par1.Vector.Count);
double randomDouble = _random.NextDouble();
var child1 = new BitVector();
var child2 = new BitVector();
if (randomDouble <= crossoverRate)
{
for (int i = 0; i < par1.Vector.Count; i++)
{
//for (int j = i; j < i + 8; j++)
//{
// if (_random.NextDouble() > 0.5)
// {
// child1.Vector.Add(par1.Vector[i]);
// child2.Vector.Add(par2.Vector[i]);
// }
// else
// {
// child1.Vector.Add(par2.Vector[i]);
// child2.Vector.Add(par1.Vector[i]);
// }
//}
if (i <= randomIndex)
{
child1.Vector.Add(par1.Vector[i]);
child2.Vector.Add(par2.Vector[i]);
}
else
{
child1.Vector.Add(par2.Vector[i]);
child2.Vector.Add(par1.Vector[i]);
}
}
}
else
{
child1.Vector = par1.Vector;
child2.Vector = par2.Vector;
}
offspring.Add(child1);
offspring.Add(child2);
return offspring;
}
public override void CalculateFitness(AbstractPhenotype phenotype)
{
var bitVector = (BitVector) phenotype.Genotype;
int count = 0;
for (int i = 0; i < GoalVector.Count; i++)
{
if (bitVector.Vector[i] == GoalVector[i])
{
count++;
}
}
phenotype.Fitness = ((double) count/bitVector.Vector.Count);
}
public override void CalculateFitness(AbstractPhenotype phenotype)
{
var pheno = (IzhikevichPhenotype) phenotype;
pheno.Train = new List<double>();
pheno.SpikeTimes = new List<int>();
pheno.MakeTrain();
pheno.SpikeTimes = CalculateSpikeTimes(pheno.Train);
const int p = 2;
switch (SDM.ToLower())
{
case "time":
pheno.Fitness = TimeDistanceMetric(SpikeTimesDataSet, pheno.SpikeTimes, p);
break;
case "interval":
pheno.Fitness = IntervalDistanceMetric(SpikeTimesDataSet, pheno.SpikeTimes, p);
break;
case "waveform":
pheno.Fitness = WaveformDistanceMetric(pheno.Train, DataSet, p);
break;
}
}
public abstract List<BitVector> Crossover(AbstractPhenotype parent1, AbstractPhenotype parent2,
double crossoverRate);
public abstract void CalculateFitness(AbstractPhenotype phenotype);
