public static OffspringSelectionGeneticAlgorithm CreateGpTimeSeriesSample() {
var problem = new SymbolicTimeSeriesPrognosisSingleObjectiveProblem();
problem.Name = "Symbolic time series prognosis problem (Mackey Glass t=17)";
problem.ProblemData.Name = "Mackey Glass t=17";
problem.MaximumSymbolicExpressionTreeLength.Value = 125;
problem.MaximumSymbolicExpressionTreeDepth.Value = 12;
problem.EvaluatorParameter.Value.HorizonParameter.Value.Value = 10;
foreach (var symbol in problem.SymbolicExpressionTreeGrammar.Symbols) {
if (symbol is Exponential || symbol is Logarithm) {
symbol.Enabled = false;
} else if (symbol is AutoregressiveTargetVariable) {
symbol.Enabled = true;
var autoRegressiveSymbol = symbol as AutoregressiveTargetVariable;
autoRegressiveSymbol.MinLag = -30;
autoRegressiveSymbol.MaxLag = -1;
}
}
var osga = new OffspringSelectionGeneticAlgorithm();
osga.Name = "Genetic Programming - Time Series Prediction (Mackey-Glass-17)";
osga.Description = "A genetic programming algorithm for creating a time-series model for the Mackey-Glass-17 time series.";
osga.Problem = problem;
SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>
(osga, popSize: 100, elites: 1, maxGens: 25, mutationRate: 0.15);
osga.MaximumSelectionPressure.Value = 100;
return osga;
}
private OffspringSelectionGeneticAlgorithm(OffspringSelectionGeneticAlgorithm original, Cloner cloner)
: base(original, cloner)
{
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
Initialize();
}
public OffspringSelectionGeneticAlgorithm CreateGeArtificialAntSample() {
OffspringSelectionGeneticAlgorithm ga = new OffspringSelectionGeneticAlgorithm();
#region Problem Configuration
var problem = new HeuristicLab.Problems.GrammaticalEvolution.GEArtificialAntProblem();
#endregion
#region Algorithm Configuration
ga.Name = "Grammatical Evolution - Artificial Ant (SantaFe)";
ga.Description = "Grammatical evolution algorithm for solving a artificial ant problem";
ga.Problem = problem;
SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
ga, 200, 1, 50, 0.05, 200);
#endregion
return ga;
}
public static OffspringSelectionGeneticAlgorithm CreateGpMultiplexerSample() {
var problem = new HeuristicLab.Problems.GeneticProgramming.Boolean.MultiplexerProblem();
problem.Name = "11-Multiplexer Problem";
problem.Encoding.TreeLength = 50;
problem.Encoding.TreeDepth = 50;
var osga = new OffspringSelectionGeneticAlgorithm();
osga.Name = "Genetic Programming - Multiplexer 11 Problem";
osga.Description = "A genetic programming algorithm that solves the 11-bit multiplexer problem.";
osga.Problem = problem;
SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, SubtreeCrossover, MultiSymbolicExpressionTreeManipulator>
(osga, popSize: 100, elites: 1, maxGens: 50, mutationRate: 0.25);
osga.MaximumSelectionPressure.Value = 200;
return osga;
}
public OffspringSelectionGeneticAlgorithm CreateGeSymbolicRegressionSample() {
var ga = new OffspringSelectionGeneticAlgorithm();
#region Problem Configuration
var problem = new HeuristicLab.Problems.GrammaticalEvolution.GESymbolicRegressionSingleObjectiveProblem();
#endregion
#region Algorithm Configuration
ga.Name = "Grammatical Evolution - Symbolic Regression (Poly-10)";
ga.Description = "Grammatical evolution algorithm for solving a symbolic regression problem problem";
ga.Problem = problem;
problem.Load(new PolyTen().GenerateRegressionData());
// must occur after loading problem data because the grammar creates symbols for random constants once the data is loaded
var consts = problem.SymbolicExpressionTreeGrammar.AllowedSymbols.OfType<Constant>().ToList();
foreach (var c in consts) {
problem.SymbolicExpressionTreeGrammar.RemoveSymbol(c);
}
SamplesUtils.ConfigureOsGeneticAlgorithmParameters<GenderSpecificSelector, Encodings.IntegerVectorEncoding.SinglePointCrossover, Encodings.IntegerVectorEncoding.UniformOnePositionManipulator>(
ga, 1000, 1, 50, 0.05, 200);
#endregion
return ga;
}
private OffspringSelectionGeneticAlgorithm(OffspringSelectionGeneticAlgorithm original, Cloner cloner)
: base(original, cloner) {
qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
Initialize();
}