public static void Xor(object target)
{
NetworkGenome genome = target as NetworkGenome;
double[] inputs = new double[2];
bool flag1 = true;
DecodedNetworks decodedNetwork = DecodedNetworks.DecodeGenome(genome);
inputs[0] = 0.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs1 = decodedNetwork.GetOutputs();
genome.Fitness = 0.0;
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs1[0]));
bool flag2 = flag1 && outputs1[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs2 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs2[0]));
bool flag3 = flag2 && outputs2[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs3 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs3[0]));
bool flag4 = flag3 && outputs3[0] >= 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs4 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs4[0]));
bool flag5 = flag4 && outputs4[0] >= 0.5;
decodedNetwork.Flush();
if (!flag5)
{
return;
}
genome.Fitness = 4.0;
}
public static bool NormalXor(List <NetworkGenome> genomes, bool test)
{
double[] inputs = new double[2];
bool flag1 = false;
foreach (NetworkGenome genome in genomes)
{
bool flag2 = true;
DecodedNetworks decodedNetwork = DecodedNetworks.DecodeGenome(genome);
inputs[0] = 0.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs1 = decodedNetwork.GetOutputs();
genome.Fitness = 0.0;
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs1[0]));
bool flag3 = flag2 && outputs1[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs2 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, 1.0 - outputs2[0]));
bool flag4 = flag3 && outputs2[0] < 0.5;
decodedNetwork.Flush();
inputs[0] = 0.0;
inputs[1] = 1.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs3 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs3[0]));
bool flag5 = flag4 && outputs3[0] >= 0.5;
decodedNetwork.Flush();
inputs[0] = 1.0;
inputs[1] = 0.0;
decodedNetwork.SetInputs(inputs);
decodedNetwork.ActivateNetwork(6);
double[] outputs4 = decodedNetwork.GetOutputs();
genome.Fitness += Math.Min(1.0, Math.Max(0.0, outputs4[0]));
bool flag6 = flag5 && outputs4[0] >= 0.5;
decodedNetwork.Flush();
if (flag6)
{
genome.Fitness = 4.0;
}
flag1 = flag1 || flag6;
}
return(flag1);
}
//based on phillip modified
static bool HyperNEATKeepaway(List <NetworkGenome> genomes, bool test)
{
if (!test)
{
int evaluations = 0;
Dictionary <int, int> evalCount = new Dictionary <int, int>();
List <int> keys = new List <int>(mapping.Keys);
for (int i = 0; i < genomes.Count; i++)
{
possibleValues.Add(i);
}
for (int i = 0; i < keys.Count; i++)
{
if (!genomes.Any(gen => gen.Id == keys[i]))
{
mapping.Remove(keys[i]);
}
else
{
possibleValues.Remove(mapping[keys[i]]);
}
}
for (int i = 0; i < genomes.Count; i++)
{
genomes[i].Fitness = 0;
for (int l = 0; l < maxVec.Length; l++)
{
genomes[i].BehaviorType.bVector[l] = 0;
}
if (!mapping.ContainsKey(genomes[i].Id))
{
DecodedNetworks net = sub.GenerateNetwork(MapWeights, DecodedNetworks.DecodeGenome(genomes[i]));
if (createdNetworks.Count < genomes.Count)
{
createdNetworks.Add(net.Clone() as DecodedNetworks);
mapping.Add(genomes[i].Id, i);
}
else
{
createdNetworks[possibleValues[0]].CopyLinks(net);
mapping.Add(genomes[i].Id, possibleValues[0]);
possibleValues.RemoveAt(0);
}
}
}
for (int i = 0; i < genomes.Count; i++)
{
//receives the fitness value and behavioral characterisation values from
//Fitness Evaluation function.
double[] fitness = new double[4];
for (int j = 0; j < config.Episodes; j++)
{
fitness = Evaluate(createdNetworks[mapping[genomes[i].Id]]);
genomes[i].RealFitness += fitness[0];
for (int v = 0; v < fitness.Length; v++)
{
genomes[i].BehaviorType.bVector[v] += fitness[v];
}
}
genomes[i].RealFitness /= config.Episodes;
for (int v = 0; v < fitness.Length; v++)
{
genomes[i].BehaviorType.bVector[v] /= config.Episodes;
}
evalCount[genomes[i].Id] = config.Episodes;
evaluations += config.Episodes;
}
List <double> lists = new List <double>();
genomes.Sort((a, b) => b.Fitness.CompareTo(a.Fitness));
double Max0 = 0; double Max1 = 0; double Max2 = 0; double fit = 0;
for (int i = 0; i < genomes.Count; i++)
{
maxVec[0] = genomes[i].RealFitness;
for (int v = 1; v < maxVec.Length; v++)
{
if (genomes[i].BehaviorType.bVector[v] > maxVec[v])
{
maxVec[v] = genomes[i].BehaviorType.bVector[v];
}
}
}
//double fitnormaliser = 11;
for (int j = 0; j < genomes.Count; j++)
{
genomes[j].RealFitness /= fitnormaliser;
genomes[j].BehaviorType.bVector[0] /= fitnormaliser;
for (int v = 1; v < maxVec.Length; v++)
{
genomes[j].BehaviorType.bVector[v] /= maxVec[v];
}
}
foreach (NetworkGenome genome in genomes)
{
genome.Novelty = genome.NoveltyMeasure.computeNovelty(genome, genomes);
genome.Genotypic_Diversity = genome.GenomeDiversity.DiversityDistance(genomes, genome) / 110;
switch (config.SearchMethod)
{
case 1:
{
genome.Fitness = genome.RealFitness; //Objective based Search
break;
}
case 2:
{
genome.Fitness = genome.Novelty; //Novelty Search
break;
}
case 3:
{
genome.Fitness = genome.RealFitness * config.scalariser + genome.Novelty * (1 - config.scalariser);
break; //Hybrid (Novelty + Objective based Search)
}
case 4:
{
genome.Fitness = genome.Genotypic_Diversity; //Genotypic Diversity Search
break;
}
case 5:
{
genome.Fitness = genome.RealFitness * config.scalariser + genome.Genotypic_Diversity * (1 - config.scalariser);
break; //Hybrid (Objective + Genotypic Diversity)
}
default:
{
genome.Fitness = genome.RealFitness;
break;
}
}
}
ComputeNovelty.addToArchive(genomes); //add the best N genomes to archive
return(false);
}
else
{
for (int i = 0; i < genomes.Count; i++)
{
if (genomes.FindIndex(g => genomes[i].Id == g.Id) < i)
{
continue;
}
genomes[i].Fitness = 0;
{
DecodedNetworks net = sub.GenerateNetwork(MapWeights, DecodedNetworks.DecodeGenome(genomes[i]));
double[] fitness = new double[4];
for (int j = 0; j < config.Episodes; j++)
{
fitness = Evaluate(createdNetworks[mapping[genomes[i].Id]]);
genomes[i].RealFitness += fitness[0];
for (int v = 0; v < fitness.Length; v++)
{
genomes[i].BehaviorType.bVector[v] += fitness[v];
}
}
genomes[i].RealFitness /= config.Episodes;
for (int v = 0; v < fitness.Length; v++)
{
genomes[i].BehaviorType.bVector[v] /= config.Episodes;
}
}
}
//double fitnormaliser = 11;
for (int i = 0; i < genomes.Count; i++)
{
maxVec[0] = genomes[i].RealFitness;
for (int v = 1; v < maxVec.Length; v++)
{
if (genomes[i].BehaviorType.bVector[v] > maxVec[v])
{
maxVec[v] = genomes[i].BehaviorType.bVector[v];
}
}
}
for (int j = 0; j < genomes.Count; j++)
{
genomes[j].RealFitness /= fitnormaliser;
genomes[j].BehaviorType.bVector[0] /= fitnormaliser;
for (int v = 1; v < maxVec.Length; v++)
{
genomes[j].BehaviorType.bVector[v] /= maxVec[v];
}
}
foreach (NetworkGenome genome in genomes)
{
genome.Novelty = genome.NoveltyMeasure.computeNovelty(genome, genomes);
genome.Genotypic_Diversity = genome.GenomeDiversity.DiversityDistance(genomes, genome) / 110;
switch (config.SearchMethod)
{
case 1:
{
genome.Fitness = genome.RealFitness; //Objective based Search
break;
}
case 2:
{
genome.Fitness = genome.Novelty; //Novelty Search
break;
}
case 3:
{
genome.Fitness = genome.RealFitness * config.scalariser + genome.Novelty * (1 - config.scalariser);
break; //Hybrid (Novelty + Objective based Search)
}
case 4:
{
genome.Fitness = genome.Genotypic_Diversity; //Genotypic Diversity Search
break;
}
case 5:
{
genome.Fitness = genome.RealFitness * config.scalariser + genome.Genotypic_Diversity * (1 - config.scalariser);
break; //Hybrid (Objective + Genotypic Diversity)
}
default:
{
genome.Fitness = genome.RealFitness;
break;
}
}
}
ComputeNovelty.addToArchive(genomes); //add the N genomes to archive
}
return(false);
}
public void GenerateLinks(LinkGen linkGen)
{
linkGen(this.sub);
this.net = DecodedNetworks.DecodeGenome(this.sub);
}
