#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . * * Author: Sabine Winkler */ #endregion using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression; namespace HeuristicLab.Problems.GrammaticalEvolution { [StorableClass("8E8968F0-EB67-4792-B7B6-859F2010B4DE")] public class GESymbolicRegressionSingleObjectiveEvaluator : GESymbolicDataAnalysisSingleObjectiveEvaluator, IGESymbolicRegressionSingleObjectiveEvaluator { public const string EvaluatorParameterName = "Evaluator"; public const string RandomParameterName = "Random"; public const string BoundsParameterName = "Bounds"; public const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength"; public IValueParameter EvaluatorParameter { get { return (IValueParameter)Parameters[EvaluatorParameterName]; } } public ILookupParameter BoundsParameter { get { return (ILookupParameter)Parameters[BoundsParameterName]; } } public ILookupParameter MaximumSymbolicExpressionTreeLengthParameter { get { return (ILookupParameter)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; } } public ISymbolicRegressionSingleObjectiveEvaluator Evaluator { get { return EvaluatorParameter.Value; } } [StorableConstructor] protected GESymbolicRegressionSingleObjectiveEvaluator(bool deserializing) : base(deserializing) { } protected GESymbolicRegressionSingleObjectiveEvaluator(GESymbolicRegressionSingleObjectiveEvaluator original, Cloner cloner) : base(original, cloner) { } public GESymbolicRegressionSingleObjectiveEvaluator() : base() { Parameters.Add(new ValueParameter(EvaluatorParameterName, "The symbolic regression evaluator that should be used to assess the quality of trees.", new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator())); Parameters.Add(new LookupParameter(BoundsParameterName, "The integer number range in which the single genomes of a genotype are created.")); Parameters.Add(new LookupParameter(MaximumSymbolicExpressionTreeLengthParameterName, "Genotype length.")); } public override IDeepCloneable Clone(Cloner cloner) { return new GESymbolicRegressionSingleObjectiveEvaluator(this, cloner); } public override bool Maximization { get { return Evaluator.Maximization; } } public override IOperation Apply() { var genotype = IntegerVectorParameter.ActualValue; // translate to phenotype var tree = GenotypeToPhenotypeMapperParameter.ActualValue.Map( RandomParameter.ActualValue, BoundsParameter.ActualValue, MaximumSymbolicExpressionTreeLengthParameter.ActualValue.Value, SymbolicExpressionTreeGrammarParameter.ActualValue, genotype ); SymbolicExpressionTreeParameter.ActualValue = tree; // write to scope for analyzers // create operation for evaluation var evalOp = ExecutionContext.CreateChildOperation(Evaluator); var successorOp = base.Apply(); return new OperationCollection(evalOp, successorOp); } } }