public Individual GenerateIndividual(List <Card> cardSet)
{
_individualsDispatched++;
return(_individualsDispatched <= _params.Search.InitialPopulation ?
Individual.GenerateRandomIndividual(cardSet) :
_featureMap.GetRandomElite().Mutate());
}
private void reset()
{
if (_featureMap.EliteMap.Count == 0)
{
_mean = LA.Vector <double> .Build.Dense(_numParams);
}
else
{
_mean = DenseVector.OfArray(_featureMap.GetRandomElite().ParamVector);
}
_direction = LA.Vector <double> .Build.Dense(_featureMap.NumFeatures);
for (int i = 0; i < _featureMap.NumFeatures; i++)
{
_direction[i] = Sampler.gaussian() * _featureMap.GetFeatureScalar(i);
}
_mutationPower = _params.MutationPower;
_pc = LA.Vector <double> .Build.Dense(_numParams);
_ps = LA.Vector <double> .Build.Dense(_numParams);
_C = new DecompMatrix(_numParams);
_individualsEvaluated = 0;
}
public Individual GenerateIndividual()
{
_individualsDispatched += 1;
if (_individualsDispatched <= _params.Search.InitialPopulation)
{
var ind = new Individual(_numParams);
for (int i = 0; i < _numParams; i++)
{
ind.ParamVector[i] = rnd.NextDouble() * 2 - 1;
}
return(ind);
}
Individual parent1 = _featureMap.GetRandomElite();
Individual parent2 = _featureMap.GetRandomElite();
var child = new Individual(_numParams);
// Perform the line mutation
double scalar1 = _params.Search.MutationPower;
double scalar2 = _params.Search.MutationPower2;
var p1 = LA.Vector <double> .Build.DenseOfEnumerable(parent1.ParamVector);
var p2 = LA.Vector <double> .Build.DenseOfEnumerable(parent2.ParamVector);
var randomVector = MathNet.Numerics.LinearAlgebra.Vector <double>
.Build.Dense(_numParams, j => gaussian(scalar1));
var lineVector = scalar2 * (p2 - p1) * gaussian(1.0);
p1 += randomVector + lineVector;
for (int i = 0; i < _numParams; i++)
{
child.ParamVector[i] = p1[i];
}
return(child);
}
private void reset()
{
if (_featureMap.EliteMap.Count == 0)
{
_mean = LA.Vector <double> .Build.Dense(_numParams);
}
else
{
_mean = DenseVector.OfArray(_featureMap.GetRandomElite().ParamVector);
}
_mutationPower = _params.MutationPower;
_pc = LA.Vector <double> .Build.Dense(_numParams);
_ps = LA.Vector <double> .Build.Dense(_numParams);
_C = new DecompMatrix(_numParams);
_individualsEvaluated = 0;
}
public Individual GenerateIndividual()
{
_individualsDispatched += 1;
if (_individualsDispatched <= _params.Search.InitialPopulation)
{
var ind = new Individual(_numParams);
for (int i = 0; i < _numParams; i++)
{
ind.ParamVector[i] = rnd.NextDouble() * 2 - 1;
}
return(ind);
}
Individual parent = _featureMap.GetRandomElite();
var child = new Individual(_numParams);
double scalar = _params.Search.MutationPower;
for (int i = 0; i < _numParams; i++)
{
child.ParamVector[i] = gaussian(scalar) + parent.ParamVector[i];
}
return(child);
}
public void Run()
{
_individualsEvaluated = 0;
_maxWins = 0;
_maxFitness = Int32.MinValue;
_runningWorkers = new Queue <int>();
_idleWorkers = new Queue <int>();
string boxesDirectory = "boxes/";
string inboxTemplate = boxesDirectory
+ "deck-{0,4:D4}-inbox.tml";
string outboxTemplate = boxesDirectory
+ "deck-{0,4:D4}-outbox.tml";
// Let the workers know we are here.
string activeDirectory = "active/";
string activeWorkerTemplate = activeDirectory
+ "worker-{0,4:D4}.txt";
string activeSearchPath = activeDirectory
+ "search.txt";
using (FileStream ow = File.Open(activeSearchPath,
FileMode.Create, FileAccess.Write, FileShare.None))
{
WriteText(ow, "MAP Elites");
WriteText(ow, _configFilename);
ow.Close();
}
Console.WriteLine("Begin search...");
while (_individualsEvaluated < _params.NumToEvaluate)
{
// Look for new workers.
string[] hailingFiles = Directory.GetFiles(activeDirectory);
foreach (string activeFile in hailingFiles)
{
string prefix = activeDirectory + "worker-";
if (activeFile.StartsWith(prefix))
{
string suffix = ".txt";
int start = prefix.Length;
int end = activeFile.Length - suffix.Length;
string label = activeFile.Substring(start, end - start);
int workerId = Int32.Parse(label);
_idleWorkers.Enqueue(workerId);
_individualStable.Add(workerId, null);
File.Delete(activeFile);
Console.WriteLine("Found worker " + workerId);
}
}
// Dispatch jobs to the available workers.
while (_idleWorkers.Count > 0)
{
if (_individualsDispatched >= _params.InitialPopulation &&
_individualsEvaluated == 0)
{
break;
}
int workerId = _idleWorkers.Dequeue();
_runningWorkers.Enqueue(workerId);
Console.WriteLine("Starting worker: " + workerId);
Individual choiceIndividual =
_individualsDispatched < _params.InitialPopulation ?
Individual.GenerateRandomIndividual(_cardSet) :
_featureMap.GetRandomElite().Mutate();
string inboxPath = string.Format(inboxTemplate, workerId);
SendWork(inboxPath, choiceIndividual);
_individualStable[workerId] = choiceIndividual;
_individualsDispatched++;
}
// Look for individuals that are done.
int numActiveWorkers = _runningWorkers.Count;
for (int i = 0; i < numActiveWorkers; i++)
{
int workerId = _runningWorkers.Dequeue();
string inboxPath = string.Format(inboxTemplate, workerId);
string outboxPath = string.Format(outboxTemplate, workerId);
// Test if this worker is done.
if (File.Exists(outboxPath) && !File.Exists(inboxPath))
{
// Wait for the file to finish being written.
Console.WriteLine("Worker done: " + workerId);
ReceiveResults(outboxPath, _individualStable[workerId]);
_featureMap.Add(_individualStable[workerId]);
_idleWorkers.Enqueue(workerId);
_individualsEvaluated++;
_map_log.UpdateLog();
}
else
{
_runningWorkers.Enqueue(workerId);
}
}
Thread.Sleep(1000);
}
// Let the workers know that we are done.
File.Delete(activeSearchPath);
}
