protected ParameterlessPopulationPyramid(ParameterlessPopulationPyramid original, Cloner cloner)
: base(original, cloner)
{
random = cloner.Clone(original.random);
pyramid = original.pyramid.Select(cloner.Clone).ToList();
tracker = cloner.Clone(original.tracker);
seen = new HashSet <BinaryVector>(original.seen.Select(cloner.Clone), new EnumerableBoolEqualityComparer());
}
private EvaluationTracker(EvaluationTracker original, Cloner cloner)
: base(original, cloner) {
problem = cloner.Clone(original.problem);
maxEvaluations = original.maxEvaluations;
BestQuality = original.BestQuality;
Evaluations = original.Evaluations;
BestFoundOnEvaluation = original.BestFoundOnEvaluation;
BestSolution = cloner.Clone(BestSolution);
}
private EvaluationTracker(EvaluationTracker original, Cloner cloner)
: base(original, cloner)
{
problem = cloner.Clone(original.problem);
maxEvaluations = original.maxEvaluations;
BestQuality = original.BestQuality;
Evaluations = original.Evaluations;
BestFoundOnEvaluation = original.BestFoundOnEvaluation;
BestSolution = cloner.Clone(original.BestSolution);
}
protected override void Run(CancellationToken cancellationToken)
{
// Set up the algorithm
if (SetSeedRandomly)
{
Seed = new System.Random().Next();
}
pyramid = new List <Population>();
seen.Clear();
random.Reset(Seed);
tracker = new EvaluationTracker(Problem, MaximumEvaluations);
// Set up the results display
Results.Add(new Result("Iterations", new IntValue(0)));
Results.Add(new Result("Evaluations", new IntValue(0)));
Results.Add(new Result("Best Solution", new BinaryVector(tracker.BestSolution)));
Results.Add(new Result("Best Quality", new DoubleValue(tracker.BestQuality)));
Results.Add(new Result("Evaluation Best Solution Was Found", new IntValue(tracker.BestFoundOnEvaluation)));
var table = new DataTable("Qualities");
table.Rows.Add(new DataRow("Best Quality"));
var iterationRows = new DataRow("Iteration Quality");
iterationRows.VisualProperties.LineStyle = DataRowVisualProperties.DataRowLineStyle.Dot;
table.Rows.Add(iterationRows);
Results.Add(new Result("Qualities", table));
table = new DataTable("Pyramid Levels");
table.Rows.Add(new DataRow("Levels"));
Results.Add(new Result("Pyramid Levels", table));
table = new DataTable("Stored Solutions");
table.Rows.Add(new DataRow("Solutions"));
Results.Add(new Result("Stored Solutions", table));
// Loop until iteration limit reached or canceled.
for (ResultsIterations = 0; ResultsIterations < MaximumIterations; ResultsIterations++)
{
double fitness = double.NaN;
try {
fitness = iterate();
cancellationToken.ThrowIfCancellationRequested();
} finally {
ResultsEvaluations = tracker.Evaluations;
ResultsBestSolution = new BinaryVector(tracker.BestSolution);
ResultsBestQuality = tracker.BestQuality;
ResultsBestFoundOnEvaluation = tracker.BestFoundOnEvaluation;
ResultsQualitiesBest.Values.Add(tracker.BestQuality);
ResultsQualitiesIteration.Values.Add(fitness);
ResultsLevels.Values.Add(pyramid.Count);
ResultsSolutions.Values.Add(seen.Count);
}
}
}
protected override void Initialize(CancellationToken cancellationToken)
{
// Set up the algorithm
if (SetSeedRandomly)
{
Seed = RandomSeedGenerator.GetSeed();
}
pyramid = new List <Population>();
seen.Clear();
random.Reset(Seed);
tracker = new EvaluationTracker(Problem, MaximumEvaluations);
// Set up the results display
Results.Add(new Result("Iterations", new IntValue(0)));
Results.Add(new Result("Evaluations", new IntValue(0)));
Results.Add(new Result("Best Solution", new BinaryVector(tracker.BestSolution)));
Results.Add(new Result("Best Quality", new DoubleValue(tracker.BestQuality)));
Results.Add(new Result("Evaluation Best Solution Was Found", new IntValue(tracker.BestFoundOnEvaluation)));
var table = new DataTable("Qualities");
table.Rows.Add(new DataRow("Best Quality"));
var iterationRows = new DataRow("Iteration Quality");
iterationRows.VisualProperties.LineStyle = DataRowVisualProperties.DataRowLineStyle.Dot;
table.Rows.Add(iterationRows);
Results.Add(new Result("Qualities", table));
table = new DataTable("Pyramid Levels");
table.Rows.Add(new DataRow("Levels"));
Results.Add(new Result("Pyramid Levels", table));
table = new DataTable("Stored Solutions");
table.Rows.Add(new DataRow("Solutions"));
Results.Add(new Result("Stored Solutions", table));
base.Initialize(cancellationToken);
}
protected override void Run(CancellationToken cancellationToken) {
// Set up the algorithm
if (SetSeedRandomly) Seed = new System.Random().Next();
pyramid = new List<Population>();
seen.Clear();
random.Reset(Seed);
tracker = new EvaluationTracker(Problem, MaximumEvaluations);
// Set up the results display
Results.Add(new Result("Iterations", new IntValue(0)));
Results.Add(new Result("Evaluations", new IntValue(0)));
Results.Add(new Result("Best Solution", new BinaryVector(tracker.BestSolution)));
Results.Add(new Result("Best Quality", new DoubleValue(tracker.BestQuality)));
Results.Add(new Result("Evaluation Best Solution Was Found", new IntValue(tracker.BestFoundOnEvaluation)));
var table = new DataTable("Qualities");
table.Rows.Add(new DataRow("Best Quality"));
var iterationRows = new DataRow("Iteration Quality");
iterationRows.VisualProperties.LineStyle = DataRowVisualProperties.DataRowLineStyle.Dot;
table.Rows.Add(iterationRows);
Results.Add(new Result("Qualities", table));
table = new DataTable("Pyramid Levels");
table.Rows.Add(new DataRow("Levels"));
Results.Add(new Result("Pyramid Levels", table));
table = new DataTable("Stored Solutions");
table.Rows.Add(new DataRow("Solutions"));
Results.Add(new Result("Stored Solutions", table));
// Loop until iteration limit reached or canceled.
for (ResultsIterations = 0; ResultsIterations < MaximumIterations; ResultsIterations++) {
double fitness = double.NaN;
try {
fitness = iterate();
cancellationToken.ThrowIfCancellationRequested();
} finally {
ResultsEvaluations = tracker.Evaluations;
ResultsBestSolution = new BinaryVector(tracker.BestSolution);
ResultsBestQuality = tracker.BestQuality;
ResultsBestFoundOnEvaluation = tracker.BestFoundOnEvaluation;
ResultsQualitiesBest.Values.Add(tracker.BestQuality);
ResultsQualitiesIteration.Values.Add(fitness);
ResultsLevels.Values.Add(pyramid.Count);
ResultsSolutions.Values.Add(seen.Count);
}
}
}