public bool measureAgainstArchive(NeatGenome.NeatGenome neatgenome, bool addToPending)
{
foreach (IGenome genome in archive)
{
double dist = BehaviorDistance.Distance(neatgenome.Behavior, ((NeatGenome.NeatGenome)genome).Behavior);
if (dist > maxDistSeen)
{
maxDistSeen = dist;
Console.WriteLine("Most novel distance: " + maxDistSeen);
}
if (dist < archive_threshold)
{
return(false);
}
}
if (addToPending)
{
pending_addition.Add(neatgenome);
}
return(true);
}
public void merge_together(GenomeList list, int size)
{
Console.WriteLine("total count: " + Convert.ToString(list.Count));
Random r = new Random();
GenomeList newList = new GenomeList();
List <bool> dirty = new List <bool>();
List <double> closest = new List <double>();
//set default values
for (int x = 0; x < list.Count; x++)
{
dirty.Add(false);
closest.Add(Double.MaxValue);
}
//now add the first individual randomly to the new population
int last_added = r.Next() % list.Count;
dirty[last_added] = true;
newList.Add(list[last_added]);
while (newList.Count < size)
{
double mostNovel = 0.0;
int mostNovelIndex = 0;
for (int x = 0; x < list.Count; x++)
{
if (dirty[x])
{
continue;
}
double dist_to_last = BehaviorDistance.Distance(((NeatGenome.NeatGenome)list[x]).Behavior,
((NeatGenome.NeatGenome)list[last_added]).Behavior);
if (dist_to_last < closest[x])
{
closest[x] = dist_to_last;
}
if (closest[x] > mostNovel)
{
mostNovel = closest[x];
mostNovelIndex = x;
}
}
dirty[mostNovelIndex] = true;
newList.Add(new NeatGenome.NeatGenome((NeatGenome.NeatGenome)list[mostNovelIndex], 0));
last_added = mostNovelIndex;
}
measure_against = newList;
}
public bool measureAgainstArchive(NeatGenome.NeatGenome neatgenome, bool addToPending)
{
foreach (IGenome genome in archive)
{
if (BehaviorDistance.Distance(neatgenome.Behavior, ((NeatGenome.NeatGenome)genome).Behavior) < archive_threshold)
{
return(false);
}
}
if (addToPending)
{
pending_addition.Add(neatgenome);
}
return(true);
}
// Returns the minimum behavioral distance to all individuals in the archive
public double valueAgainstArchive(IGenome neatgenome)
{
double minSoFar = Double.MaxValue;
double temp;
foreach (IGenome genome in archive)
{
if (genome.Equals(neatgenome))
{
continue; // Don't let a genome be tested against itself, or else we'd always return 0
}
temp = BehaviorDistance.Distance(((NeatGenome.NeatGenome)neatgenome).Behavior, ((NeatGenome.NeatGenome)genome).Behavior);
if (temp < minSoFar)
{
minSoFar = temp;
}
}
return(minSoFar);
}
//measure the novelty of an organism against the fixed population
public double measureNovelty(NeatGenome.NeatGenome neatgenome)
{
double sum = 0.0;
if (!initialized)
{
return(Double.MinValue);
}
List <Pair <double, NeatGenome.NeatGenome> > noveltyList = new List <Pair <double, NeatGenome.NeatGenome> >();
foreach (IGenome genome in measure_against)
{
noveltyList.Add(new Pair <double, NeatGenome.NeatGenome>(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior, neatgenome.Behavior), ((NeatGenome.NeatGenome)genome)));
}
foreach (IGenome genome in archive)
{
noveltyList.Add(new Pair <double, NeatGenome.NeatGenome>(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior, neatgenome.Behavior), ((NeatGenome.NeatGenome)genome)));
// noveltyList.Add(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior,neatgenome.Behavior));
}
//see if we should add this genome to the archive
measureAgainstArchive(neatgenome, true);
noveltyList.Sort();
int nn = nearest_neighbors;
if (noveltyList.Count < nearest_neighbors)
{
nn = noveltyList.Count;
}
for (int x = 0; x < nn; x++)
{
sum += noveltyList[x].First;
if (neatgenome.RealFitness > noveltyList[x].Second.RealFitness)
{
neatgenome.competition += 1;
}
noveltyList[x].Second.locality += 1;
// sum+=10000.0; //was 100
}
return(Math.Max(sum, EvolutionAlgorithm.MIN_GENOME_FITNESS));
}
//measure the novelty of an organism against the fixed population
public double measureNovelty(NeatGenome.NeatGenome neatgenome)
{
double sum = 0.0;
if (!initialized)
{
//Console.WriteLine("NOVELTY NOT INITIALIZED"); //JUSTIN: Debug, Remove this!
return(Double.MinValue);
}
List <Pair <double, NeatGenome.NeatGenome> > noveltyList = new List <Pair <double, NeatGenome.NeatGenome> >();
foreach (IGenome genome in measure_against)
{
noveltyList.Add(new Pair <double, NeatGenome.NeatGenome>(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior, neatgenome.Behavior), ((NeatGenome.NeatGenome)genome)));
}
/*foreach(IGenome genome in archive) //JUSTIN: Deciding not to use the archive in the neighborhood. Hope this helps?
* {
* noveltyList.Add(new Pair<double,NeatGenome.NeatGenome>(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior,neatgenome.Behavior),((NeatGenome.NeatGenome)genome)));
* // noveltyList.Add(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior,neatgenome.Behavior));
* }//*/
//Console.WriteLine("NOVELTYLIST SIZE: " + noveltyList.Count + " m_a: " + measure_against.Count + " archive: " + archive.Count);
//see if we should add this genome to the archive
if (usingProbabilisticArchive)
{
probabalisticMeasureAgainstArchive(neatgenome);
}
else
{
measureAgainstArchive(neatgenome, true);
}
noveltyList.Sort();
int nn = nearest_neighbors;
if (noveltyList.Count < nearest_neighbors)
{
nn = noveltyList.Count;
}
for (int x = 0; x < nn; x++)
{
sum += noveltyList[x].First;
if (neatgenome.RealFitness == 0 || noveltyList[x].Second.RealFitness == 0)
{
Console.WriteLine("WARNING! SOMEBODY IS ZERO!");
}
if (neatgenome.RealFitness > noveltyList[x].Second.RealFitness)
{
neatgenome.competition += 1;
//Console.WriteLine(neatgenome.RealFitness + " > " + (noveltyList[x].Second).RealFitness + " so incrementing competition");
}
//if (neatgenome.objectives[neatgenome.objectives.Length - 1] > noveltyList[x].Second.objectives[neatgenome.objectives.Length - 1])
//neatgenome.localGenomeNovelty += 1;
if (neatgenome.geneticDiversity > noveltyList[x].Second.geneticDiversity)
{
neatgenome.localGenomeNovelty += 1;
//Console.WriteLine("localGenomeNovelty: " + neatgenome.geneticDiversity + " > " + noveltyList[x].Second.geneticDiversity);
}
noveltyList[x].Second.locality += 1;
neatgenome.nearestNeighbors++;
// sum+=10000.0; //was 100
}
return(Math.Max(sum, EvolutionAlgorithm.MIN_GENOME_FITNESS));
}
//measure the novelty of an organism against the fixed population
public double measureNovelty(NeatGenome.NeatGenome neatgenome)
{
double sum = 0.0;
if (!initialized)
{
return(Double.MinValue);
}
List <Pair <double, NeatGenome.NeatGenome> > noveltyList = new List <Pair <double, NeatGenome.NeatGenome> >();
foreach (IGenome genome in measure_against)
{
noveltyList.Add(new Pair <double, NeatGenome.NeatGenome>(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior, neatgenome.Behavior), ((NeatGenome.NeatGenome)genome)));
}
foreach (IGenome genome in archive)
{
noveltyList.Add(new Pair <double, NeatGenome.NeatGenome>(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior, neatgenome.Behavior), ((NeatGenome.NeatGenome)genome)));
// noveltyList.Add(BehaviorDistance.Distance(((NeatGenome.NeatGenome)genome).Behavior,neatgenome.Behavior));
}
//see if we should add this genome to the archive
measureAgainstArchive(neatgenome, true);
noveltyList.Sort();
int nn = nearest_neighbors;
if (noveltyList.Count < nearest_neighbors)
{
nn = noveltyList.Count;
}
neatgenome.nearestNeighbors = nn;
//Paul - reset local competition and local genome novelty -- might have been incrementing over time
neatgenome.competition = 0;
neatgenome.localGenomeNovelty = 0;
for (int x = 0; x < nn; x++)
{
sum += noveltyList[x].First;
if (neatgenome.RealFitness > noveltyList[x].Second.RealFitness)
{
neatgenome.competition += 1;
}
if (neatgenome.objectives[neatgenome.objectives.Length - 1] > noveltyList[x].Second.objectives[neatgenome.objectives.Length - 1])
{
neatgenome.localGenomeNovelty += 1;
}
noveltyList[x].Second.locality += 1;
// sum+=10000.0; //was 100
}
//neatgenome.locality = 0;
//for(int x=0;x<nn;x++)
//{
// sum+=noveltyList[x].First;
// if(neatgenome.RealFitness>noveltyList[x].Second.RealFitness)
// neatgenome.competition+=1;
// noveltyList[x].Second.locality+=1;
// //Paul: This might not be the correct meaning of locality, but I am hijacking it instead
// //count how many genomes we are neighbored to
// //then, if we take neatgenome.competition/neatgenome.locality - we get percentage of genomes that were beaten locally!
// neatgenome.locality += 1;
// // sum+=10000.0; //was 100
//}
return(Math.Max(sum, EvolutionAlgorithm.MIN_GENOME_FITNESS));
}