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; }