Changeset 12547

Timestamp:

06/30/15 12:32:08 (9 years ago)

Author:

mkommend

Message:

#2175: Merged trunk changes.

Location:

branches/DataAnalysis.ComplexityAnalyzer

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4 (modified) (1 prop)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionModel.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPhenotypicDiversityAnalyzer.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningAnalyzer.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisModelComplexityAnalyzer.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisSingleObjectivePruningAnalyzer.cs (modified) (10 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/MultiSymbolicDataAnalysisExpressionCrossover.cs (modified) (5 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionContextAwareCrossover.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDepthConstrainedCrossover.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDeterministicBestCrossover.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Formatters/SymbolicDataAnalysisExpressionCSharpFormatter.cs (copied) (copied from trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Formatters/SymbolicDataAnalysisExpressionCSharpFormatter.cs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Formatters/SymbolicDataAnalysisExpressionExcelFormatter.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interfaces/ISymbolicDataAnalysisExpressionTreeInterpreter.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeILEmittingInterpreter.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeInterpreter.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeLinearInterpreter.cs (modified) (3 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisExpressionPruningOperator.cs (modified) (8 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisExpressionTreeSimplifier.cs (modified) (9 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisProblem.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisSolutionImpactValuesCalculator.cs (modified) (1 diff)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableConditionTreeNode.cs (modified) (2 diffs)
• HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableTreeNode.cs (modified) (2 diffs)

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveProblem.cs

@@ -29 +29 @@
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Symbolic Regression Problem (multi objective)", "Represents a multi objective symbolic regression problem.")]
+  [Item("Symbolic Regression Problem (multi-objective)", "Represents a multi objective symbolic regression problem.")]
   [StorableClass]
-  [Creatable("Problems")]
+  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 110)]
   public class SymbolicRegressionMultiObjectiveProblem : SymbolicDataAnalysisMultiObjectiveProblem<IRegressionProblemData, ISymbolicRegressionMultiObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem {
     private const double PunishmentFactor = 10;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionConstantOptimizationEvaluator.cs

@@ -202 +202 @@
       int info;
 
-      Dataset ds = problemData.Dataset;
+      IDataset ds = problemData.Dataset;
       double[,] x = new double[rows.Count(), variableNames.Count];
       int row = 0;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/SymbolicRegressionSingleObjectiveProblem.cs

@@ -28 +28 @@
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
-  [Item("Symbolic Regression Problem (single objective)", "Represents a single objective symbolic regression problem.")]
+  [Item("Symbolic Regression Problem (single-objective)", "Represents a single objective symbolic regression problem.")]
   [StorableClass]
-  [Creatable("Problems")]
+  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 100)]
   public class SymbolicRegressionSingleObjectiveProblem : SymbolicDataAnalysisSingleObjectiveProblem<IRegressionProblemData, ISymbolicRegressionSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem {
     private const double PunishmentFactor = 10;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionModel.cs

@@ -47 +47 @@
     }
 
-    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
       return Interpreter.GetSymbolicExpressionTreeValues(SymbolicExpressionTree, dataset, rows)
         .LimitToRange(LowerEstimationLimit, UpperEstimationLimit);

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPhenotypicDiversityAnalyzer.cs

@@ -35 +35 @@
   [StorableClass]
   public class SymbolicRegressionPhenotypicDiversityAnalyzer : PopulationSimilarityAnalyzer,
-    ISymbolicDataAnalysisBoundedOperator, ISymbolicDataAnalysisInterpreterOperator, ISymbolicExpressionTreeOperator {
+    ISymbolicDataAnalysisBoundedOperator, ISymbolicDataAnalysisInterpreterOperator, ISymbolicExpressionTreeAnalyzer {
     #region parameter names
     private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningAnalyzer.cs

@@ -31 +31 @@
   [StorableClass]
   public sealed class SymbolicRegressionPruningAnalyzer : SymbolicDataAnalysisSingleObjectivePruningAnalyzer {
-    private const string ImpactValuesCalculatorParameterName = "ImpactValuesCalculator";
     private const string PruningOperatorParameterName = "PruningOperator";
-    private SymbolicRegressionPruningAnalyzer(SymbolicRegressionPruningAnalyzer original, Cloner cloner)
-      : base(original, cloner) {
+    public IValueParameter<SymbolicRegressionPruningOperator> PruningOperatorParameter {
+      get { return (IValueParameter<SymbolicRegressionPruningOperator>)Parameters[PruningOperatorParameterName]; }
     }
-    public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicRegressionPruningAnalyzer(this, cloner);
+
+    protected override SymbolicDataAnalysisExpressionPruningOperator PruningOperator {
+      get { return PruningOperatorParameter.Value; }
     }
+
+    private SymbolicRegressionPruningAnalyzer(SymbolicRegressionPruningAnalyzer original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionPruningAnalyzer(this, cloner); }
 
     [StorableConstructor]
@@ -44 +47 @@
 
     public SymbolicRegressionPruningAnalyzer() {
-      Parameters.Add(new ValueParameter<SymbolicDataAnalysisSolutionImpactValuesCalculator>(ImpactValuesCalculatorParameterName, "The impact values calculator", new SymbolicRegressionSolutionImpactValuesCalculator()));
-      Parameters.Add(new ValueParameter<SymbolicDataAnalysisExpressionPruningOperator>(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicRegressionPruningOperator(new SymbolicRegressionSolutionImpactValuesCalculator())));
+      Parameters.Add(new ValueParameter<SymbolicRegressionPruningOperator>(PruningOperatorParameterName, "The operator used to prune trees", new SymbolicRegressionPruningOperator(new SymbolicRegressionSolutionImpactValuesCalculator())));
     }
   }

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SymbolicRegressionPruningOperator.cs

@@ -53 +53 @@
     protected override double Evaluate(IDataAnalysisModel model) {
       var regressionModel = (IRegressionModel)model;
-      var regressionProblemData = (IRegressionProblemData)ProblemData;
-      var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
+      var regressionProblemData = (IRegressionProblemData)ProblemDataParameter.ActualValue;
+      var rows = Enumerable.Range(FitnessCalculationPartitionParameter.ActualValue.Start, FitnessCalculationPartitionParameter.ActualValue.Size);
       return Evaluate(regressionModel, regressionProblemData, rows);
     }
@@ -71 +71 @@
       var clonedTree = (ISymbolicExpressionTree)tree.Clone();
       var model = new SymbolicRegressionModel(clonedTree, interpreter, estimationLimits.Lower, estimationLimits.Upper);
-      var nodes = clonedTree.IterateNodesPrefix().ToList();
+      var nodes = clonedTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList(); // skip the nodes corresponding to the ProgramRootSymbol and the StartSymbol
       double quality = Evaluate(model, problemData, rows);
 
@@ -81 +81 @@
         impactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, quality);
 
-        if (pruneOnlyZeroImpactNodes) {
-          if (!impactValue.IsAlmost(0.0)) continue;
-        } else if (nodeImpactThreshold < impactValue) {
-          continue;
-        }
+        if (pruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
+        if (!pruneOnlyZeroImpactNodes && impactValue > nodeImpactThreshold) continue;
 
         var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisModelComplexityAnalyzer.cs

@@ -53 +53 @@
             for (int i = 0; i < node.SubtreeCount; i++) {
               var nodeComplexity = CalculateComplexity(node.GetSubtree(i));
-              complexity *= nodeComplexity > 2 ? nodeComplexity : 2;
+              complexity *= nodeComplexity + 1;
             }
             return complexity;
@@ -61 +61 @@
             for (int i = 0; i < node.SubtreeCount; i++) {
               var nodeComplexity = CalculateComplexity(node.GetSubtree(i));
-              complexity *= nodeComplexity > 2 ? nodeComplexity : 2;
+              complexity *= nodeComplexity +1;
             }
             return complexity;
@@ -75 +75 @@
         case OpCodes.Tan: {
             double complexity = CalculateComplexity(node.GetSubtree(0));
-            return Math.Pow(2.0, complexity); ;
+            return Math.Pow(2.0, complexity);
           }
         case OpCodes.Exp: {

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Analyzers/SymbolicDataAnalysisSingleObjectivePruningAnalyzer.cs

@@ -45 +45 @@
     private const string TotalNumberOfPrunedSubtreesParameterName = "Number of pruned subtrees";
     private const string TotalNumberOfPrunedTreesParameterName = "Number of pruned trees";
+    private const string TotalNumberOfPrunedNodesParameterName = "Number of pruned nodes";
     private const string RandomParameterName = "Random";
-    private const string PruneOnlyZeroImpactNodesParameterName = "PruneOnlyZeroImpactNodes";
-    private const string NodeImpactThresholdParameterName = "ImpactThreshold";
-    private const string PruningOperatorParameterName = "PruningOperator";
     private const string ResultsParameterName = "Results";
     private const string PopulationSizeParameterName = "PopulationSize";
@@ -54 +52 @@
 
     #region private members
+    private DataReducer prunedNodesReducer;
     private DataReducer prunedSubtreesReducer;
     private DataReducer prunedTreesReducer;
@@ -61 +60 @@
 
     #region parameter properties
-    public IValueParameter<SymbolicDataAnalysisExpressionPruningOperator> PruningOperatorParameter {
-      get { return (IValueParameter<SymbolicDataAnalysisExpressionPruningOperator>)Parameters[PruningOperatorParameterName]; }
-    }
-    public IFixedValueParameter<BoolValue> PruneOnlyZeroImpactNodesParameter {
-      get { return (IFixedValueParameter<BoolValue>)Parameters[PruneOnlyZeroImpactNodesParameterName]; }
-    }
-    public IFixedValueParameter<DoubleValue> NodeImpactThresholdParameter {
-      get { return (IFixedValueParameter<DoubleValue>)Parameters[NodeImpactThresholdParameterName]; }
-    }
     public ILookupParameter<IRandom> RandomParameter {
       get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
@@ -91 +81 @@
 
     #region properties
-    protected SymbolicDataAnalysisExpressionPruningOperator PruningOperator { get { return PruningOperatorParameter.Value; } }
+    protected abstract SymbolicDataAnalysisExpressionPruningOperator PruningOperator { get; }
     protected int UpdateInterval { get { return UpdateIntervalParameter.Value.Value; } }
 
@@ -112 +102 @@
       get { return PruningProbabilityParameter.Value.Value; }
       set { PruningProbabilityParameter.Value.Value = value; }
-    }
-
-    protected bool PruneOnlyZeroImpactNodes {
-      get { return PruneOnlyZeroImpactNodesParameter.Value.Value; }
-      set { PruneOnlyZeroImpactNodesParameter.Value.Value = value; }
-    }
-    protected double NodeImpactThreshold {
-      get { return NodeImpactThresholdParameter.Value.Value; }
-      set { NodeImpactThresholdParameter.Value.Value = value; }
     }
     #endregion
@@ -140 +121 @@
     protected SymbolicDataAnalysisSingleObjectivePruningAnalyzer(SymbolicDataAnalysisSingleObjectivePruningAnalyzer original, Cloner cloner)
       : base(original, cloner) {
+      if (original.prunedNodesReducer != null)
+        this.prunedNodesReducer = (DataReducer)original.prunedNodesReducer.Clone();
       if (original.prunedSubtreesReducer != null)
         this.prunedSubtreesReducer = (DataReducer)original.prunedSubtreesReducer.Clone();
@@ -197 +180 @@
       Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "The random number generator."));
       Parameters.Add(new LookupParameter<IDataAnalysisProblemData>(ProblemDataParameterName, "The problem data."));
-      Parameters.Add(new FixedValueParameter<DoubleValue>(NodeImpactThresholdParameterName, "The impact threshold  below which an individual should be pruned.", new DoubleValue(0.0)));
-      Parameters.Add(new FixedValueParameter<BoolValue>(PruneOnlyZeroImpactNodesParameterName, "Switch to determine of only zero impact individuals should be pruned.", new BoolValue(false)));
       Parameters.Add(new LookupParameter<IntValue>(PopulationSizeParameterName, "The population of individuals."));
       #endregion
@@ -250 +231 @@
       UpdateCounter = 0;
 
-      if (prunedSubtreesReducer == null || prunedTreesReducer == null || valuesCollector == null || resultsCollector == null) { InitializeOperators(); }
+      if (prunedNodesReducer == null || prunedSubtreesReducer == null || prunedTreesReducer == null || valuesCollector == null || resultsCollector == null) { InitializeOperators(); }
 
       var prune = CreatePruningOperation();
+      var reducePrunedNodes = ExecutionContext.CreateChildOperation(prunedNodesReducer);
       var reducePrunedSubtrees = ExecutionContext.CreateChildOperation(prunedSubtreesReducer);
       var reducePrunedTrees = ExecutionContext.CreateChildOperation(prunedTreesReducer);
@@ -258 +240 @@
       var collectResults = ExecutionContext.CreateChildOperation(resultsCollector);
 
-      return new OperationCollection { prune, reducePrunedSubtrees, reducePrunedTrees, collectValues, collectResults, base.Apply() };
+      return new OperationCollection { prune, reducePrunedNodes, reducePrunedSubtrees, reducePrunedTrees, collectValues, collectResults, base.Apply() };
     }
 
     private void InitializeOperators() {
+      prunedNodesReducer = new DataReducer();
+      prunedNodesReducer.ParameterToReduce.ActualName = PruningOperator.PrunedNodesParameter.ActualName;
+      prunedNodesReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum); // sum all the pruned subtrees parameter values
+      prunedNodesReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Assign); // asign the sum to the target parameter
+      prunedNodesReducer.TargetParameter.ActualName = TotalNumberOfPrunedNodesParameterName;
+
       prunedSubtreesReducer = new DataReducer();
       prunedSubtreesReducer.ParameterToReduce.ActualName = PruningOperator.PrunedSubtreesParameter.ActualName;
@@ -275 +263 @@
 
       valuesCollector = new DataTableValuesCollector();
+      valuesCollector.CollectedValues.Add(new LookupParameter<IntValue>(TotalNumberOfPrunedNodesParameterName));
       valuesCollector.CollectedValues.Add(new LookupParameter<IntValue>(TotalNumberOfPrunedSubtreesParameterName));
       valuesCollector.CollectedValues.Add(new LookupParameter<IntValue>(TotalNumberOfPrunedTreesParameterName));

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/MultiSymbolicDataAnalysisExpressionCrossover.cs

@@ -35 +35 @@
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
   [Item("MultiSymbolicDataAnalysisExpressionCrossover", "Randomly selects and applies one of its crossovers every time it is called.")]
+  [StorableClass]
   public class MultiSymbolicDataAnalysisExpressionCrossover<T> : StochasticMultiBranch<ISymbolicExpressionTreeCrossover>,
     ISymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
     private const string ParentsParameterName = "Parents";
-    private const string ChildParameterName = "Child";
+    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
     private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
     private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
@@ -62 +63 @@
       get { return (ScopeTreeLookupParameter<ISymbolicExpressionTree>)Parameters[ParentsParameterName]; }
     }
-    public ILookupParameter<ISymbolicExpressionTree> ChildParameter {
-      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[ChildParameterName]; }
+    public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
+      get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
     }
     public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
@@ -99 +100 @@
       Parameters.Add(new ValueLookupParameter<IntRange>(SymbolicDataAnalysisEvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
       Parameters.Add(new ScopeTreeLookupParameter<ISymbolicExpressionTree>(ParentsParameterName, "The parent symbolic expression trees which should be crossed."));
-      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(ChildParameterName, "The child symbolic expression tree resulting from the crossover."));
+      Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The child symbolic expression tree resulting from the crossover."));
 
       EvaluatorParameter.Hidden = true;
@@ -111 +112 @@
 
       SelectedOperatorParameter.ActualName = "SelectedCrossoverOperator";
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.4
+      if (!Parameters.ContainsKey(SymbolicExpressionTreeParameterName))
+        Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression tree on which the operator should be applied."));
+      #endregion
     }
 
@@ -155 +165 @@
     private void ParameterizeCrossovers() {
       foreach (ISymbolicExpressionTreeCrossover op in Operators) {
-        op.ChildParameter.ActualName = ChildParameter.Name;
+        op.SymbolicExpressionTreeParameter.ActualName = SymbolicExpressionTreeParameter.Name;
         op.ParentsParameter.ActualName = ParentsParameter.Name;
       }

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionContextAwareCrossover.cs

@@ -27 +27 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -68 +69 @@
           possibleChildren.Add(n);
       });
-      var selectedChild = possibleChildren.SelectRandom(random);
+
+      var selectedChild = possibleChildren.SampleRandom(random);
       var crossoverPoints = new List<CutPoint>();
       var qualities = new List<Tuple<CutPoint, double>>();

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDepthConstrainedCrossover.cs

@@ -29 +29 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -115 +116 @@
         throw new Exception("No crossover points available in the first parent");
 
-      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
-
+      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
@@ -126 +126 @@
                              select s).ToList();
       if (allowedBranches.Count == 0) return parent0;
-      var selectedBranch = allowedBranches.SelectRandom(random);
+
+      var selectedBranch = allowedBranches.SampleRandom(random);
       Swap(crossoverPoint0, selectedBranch);
       return parent0;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDeterministicBestCrossover.cs

@@ -27 +27 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -67 +68 @@
           crossoverPoints0.Add(new CutPoint(n.Parent, n));
       });
-      CutPoint crossoverPoint0 = crossoverPoints0.SelectRandom(random);
+
+      CutPoint crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover.cs

@@ -27 +27 @@
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -69 +70 @@
         }
       });
-      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
+
+      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
@@ -137 +139 @@
           weights[i] /= sum;
 
+#pragma warning disable 612, 618
         selectedBranch = allowedBranches.SelectRandom(weights, random);
+#pragma warning restore 612, 618
       }
       Swap(crossoverPoint0, selectedBranch);

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs

@@ -28 +28 @@
 using HeuristicLab.Parameters;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
@@ -81 +82 @@
           crossoverPoints0.Add(new CutPoint(n.Parent, n));
       });
-      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
+
+      var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
       int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
       int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Formatters/SymbolicDataAnalysisExpressionExcelFormatter.cs

@@ -71 +71 @@
     }
 
-    public string Format(ISymbolicExpressionTree symbolicExpressionTree, Dataset dataset) {
+    public string Format(ISymbolicExpressionTree symbolicExpressionTree, IDataset dataset) {
       var stringBuilder = new StringBuilder();
       if (dataset != null) CalculateVariableMapping(symbolicExpressionTree, dataset);
@@ -85 +85 @@
     }
 
-    private void CalculateVariableMapping(ISymbolicExpressionTree tree, Dataset dataset) {
+    private void CalculateVariableMapping(ISymbolicExpressionTree tree, IDataset dataset) {
       int columnIndex = 0;
       int inputIndex = 0;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj

@@ -198 +198 @@
       <SubType>Code</SubType>
     </Compile>
+    <Compile Include="Formatters\SymbolicDataAnalysisExpressionCSharpFormatter.cs" />
     <Compile Include="Importer\SymbolicExpressionImporter.cs" />
     <Compile Include="Importer\Token.cs" />

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interfaces/ISymbolicDataAnalysisExpressionTreeInterpreter.cs

@@ -27 +27 @@
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
   public interface ISymbolicDataAnalysisExpressionTreeInterpreter : INamedItem, IStatefulItem {
-    IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows);
+    IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows);
     IntValue EvaluatedSolutions { get; set; }
   }

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeILEmittingInterpreter.cs

@@ -138 +138 @@
     #endregion
 
-    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
       if (CheckExpressionsWithIntervalArithmetic.Value)
         throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
@@ -161 +161 @@
     }
 
-    private InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, Dataset dataset) {
+    private InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, IDataset dataset) {
       Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
       Dictionary<string, int> doubleVariableNames = dataset.DoubleVariables.Select((x, i) => new { x, i }).ToDictionary(e => e.x, e => e.i);
@@ -182 +182 @@
     }
 
-    private void CompileInstructions(ILGenerator il, InterpreterState state, Dataset ds) {
+    private void CompileInstructions(ILGenerator il, InterpreterState state, IDataset ds) {
       Instruction currentInstr = state.NextInstruction();
       int nArgs = currentInstr.nArguments;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeInterpreter.cs

@@ -95 +95 @@
     #endregion
 
-    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
       if (CheckExpressionsWithIntervalArithmetic.Value)
         throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
@@ -111 +111 @@
     }
 
-    private static InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, Dataset dataset) {
+    private static InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, IDataset dataset) {
       Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
       int necessaryArgStackSize = 0;
@@ -132 +132 @@
 
 
-    public virtual double Evaluate(Dataset dataset, ref int row, InterpreterState state) {
+    public virtual double Evaluate(IDataset dataset, ref int row, InterpreterState state) {
       Instruction currentInstr = state.NextInstruction();
       switch (currentInstr.opCode) {

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeLinearInterpreter.cs

@@ -102 +102 @@
     #endregion
 
-    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows) {
+    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
       if (CheckExpressionsWithIntervalArithmetic.Value)
         throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
@@ -115 +115 @@
     }
 
-    private double Evaluate(Dataset dataset, int row, LinearInstruction[] code) {
+    private double Evaluate(IDataset dataset, int row, LinearInstruction[] code) {
       for (int i = code.Length - 1; i >= 0; --i) {
         if (code[i].skip) continue;
@@ -352 +352 @@
     }
 
-    public static void PrepareInstructions(LinearInstruction[] code, Dataset dataset) {
+    public static void PrepareInstructions(LinearInstruction[] code, IDataset dataset) {
       for (int i = 0; i != code.Length; ++i) {
         var instr = code[i];

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisExpressionPruningOperator.cs

@@ -34 +34 @@
   [StorableClass]
   [Item("SymbolicExpressionTreePruningOperator", "An operator that replaces introns with constant values in a symbolic expression tree.")]
-  public abstract class SymbolicDataAnalysisExpressionPruningOperator : SingleSuccessorOperator {
+  public abstract class SymbolicDataAnalysisExpressionPruningOperator : SingleSuccessorOperator, ISymbolicExpressionTreeOperator {
     #region parameter names
     private const string ProblemDataParameterName = "ProblemData";
@@ -41 +41 @@
     private const string PrunedSubtreesParameterName = "PrunedSubtrees";
     private const string PrunedTreesParameterName = "PrunedTrees";
+    private const string PrunedNodesParameterName = "PrunedNodes";
     private const string FitnessCalculationPartitionParameterName = "FitnessCalculationPartition";
     private const string NodeImpactThresholdParameterName = "ImpactThreshold";
@@ -72 +73 @@
       get { return (ILookupParameter<IntValue>)Parameters[PrunedTreesParameterName]; }
     }
+    public ILookupParameter<IntValue> PrunedNodesParameter {
+      get { return (ILookupParameter<IntValue>)Parameters[PrunedNodesParameterName]; }
+    }
     public IFixedValueParameter<DoubleValue> NodeImpactThresholdParameter {
       get { return (IFixedValueParameter<DoubleValue>)Parameters[NodeImpactThresholdParameterName]; }
@@ -87 +91 @@
 
     #region properties
-    protected IDataAnalysisProblemData ProblemData { get { return ProblemDataParameter.ActualValue; } }
-    protected ISymbolicDataAnalysisSolutionImpactValuesCalculator ImpactValuesCalculator { get { return ImpactValuesCalculatorParameter.Value; } }
-    protected IntRange FitnessCalculationPartition { get { return FitnessCalculationPartitionParameter.ActualValue; } }
-    protected bool PruneOnlyZeroImpactNodes {
+    public ISymbolicDataAnalysisSolutionImpactValuesCalculator ImpactValuesCalculator {
+      get { return ImpactValuesCalculatorParameter.Value; }
+      set { ImpactValuesCalculatorParameter.Value = value; }
+    }
+    public bool PruneOnlyZeroImpactNodes {
       get { return PruneOnlyZeroImpactNodesParameter.Value.Value; }
       set { PruneOnlyZeroImpactNodesParameter.Value.Value = value; }
     }
-    protected double NodeImpactThreshold {
+    public double NodeImpactThreshold {
       get { return NodeImpactThresholdParameter.Value.Value; }
       set { NodeImpactThresholdParameter.Value.Value = value; }
     }
-    protected ISymbolicExpressionTree SymbolicExpressionTree { get { return SymbolicExpressionTreeParameter.ActualValue; } }
-    protected DoubleValue Quality { get { return QualityParameter.ActualValue; } }
-    protected DoubleLimit EstimationLimits { get { return EstimationLimitsParameter.ActualValue; } }
-    protected ISymbolicDataAnalysisExpressionTreeInterpreter Interpreter { get { return InterpreterParameter.ActualValue; } }
     #endregion
 
@@ -114 +115 @@
       Parameters.Add(new LookupParameter<ISymbolicDataAnalysisModel>(SymbolicDataAnalysisModelParameterName));
       Parameters.Add(new LookupParameter<IntRange>(FitnessCalculationPartitionParameterName));
+      Parameters.Add(new LookupParameter<IntValue>(PrunedNodesParameterName, "A counter of how many nodes were pruned."));
       Parameters.Add(new LookupParameter<IntValue>(PrunedSubtreesParameterName, "A counter of how many subtrees were replaced."));
       Parameters.Add(new LookupParameter<IntValue>(PrunedTreesParameterName, "A counter of how many trees were pruned."));
@@ -131 +133 @@
 
     public override IOperation Apply() {
-      var model = CreateModel(SymbolicExpressionTree, Interpreter, ProblemData, EstimationLimits);
-      var nodes = SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
-      var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
+      var tree = SymbolicExpressionTreeParameter.ActualValue;
+      var problemData = ProblemDataParameter.ActualValue;
+      var fitnessCalculationPartition = FitnessCalculationPartitionParameter.ActualValue;
+      var estimationLimits = EstimationLimitsParameter.ActualValue;
+      var interpreter = InterpreterParameter.ActualValue;
+
+      var model = CreateModel(tree, interpreter, problemData, estimationLimits);
+      var nodes = tree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
+      var rows = Enumerable.Range(fitnessCalculationPartition.Start, fitnessCalculationPartition.Size);
       var prunedSubtrees = 0;
       var prunedTrees = 0;
+      var prunedNodes = 0;
 
       double quality = Evaluate(model);
@@ -144 +153 @@
 
         double impactValue, replacementValue;
-        ImpactValuesCalculator.CalculateImpactAndReplacementValues(model, node, ProblemData, rows, out impactValue, out replacementValue, quality);
+        ImpactValuesCalculator.CalculateImpactAndReplacementValues(model, node, problemData, rows, out impactValue, out replacementValue, quality);
 
-        if (PruneOnlyZeroImpactNodes) {
-          if (!impactValue.IsAlmost(0.0)) continue;
-        } else if (NodeImpactThreshold < impactValue) {
-          continue;
-        }
+        if (PruneOnlyZeroImpactNodes && !impactValue.IsAlmost(0.0)) continue;
+        if (!PruneOnlyZeroImpactNodes && impactValue > NodeImpactThreshold) continue;
 
         var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
         constantNode.Value = replacementValue;
 
+        var length = node.GetLength();
         ReplaceWithConstant(node, constantNode);
-        i += node.GetLength() - 1; // skip subtrees under the node that was folded
+        i += length - 1; // skip subtrees under the node that was folded
 
         quality -= impactValue;
-
         prunedSubtrees++;
+        prunedNodes += length;
       }
 
@@ -166 +173 @@
       PrunedSubtreesParameter.ActualValue = new IntValue(prunedSubtrees);
       PrunedTreesParameter.ActualValue = new IntValue(prunedTrees);
+      PrunedNodesParameter.ActualValue = new IntValue(prunedNodes);
+      QualityParameter.ActualValue.Value = quality;
 
       return base.Apply();
-    }
-
-    public ISymbolicExpressionTree Prune(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDataAnalysisProblemData problemData, DoubleLimit estimationLimits) {
-      var model = CreateModel((ISymbolicExpressionTree)tree.Clone(), Interpreter, ProblemData, EstimationLimits);
-      var nodes = SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0).IterateNodesPrefix().ToList();
-      var rows = Enumerable.Range(FitnessCalculationPartition.Start, FitnessCalculationPartition.Size);
-
-      double quality = Evaluate(model);
-
-      for (int i = 0; i < nodes.Count; ++i) {
-        var node = nodes[i];
-        if (node is ConstantTreeNode) continue;
-
-        double impactValue, replacementValue;
-        ImpactValuesCalculator.CalculateImpactAndReplacementValues(model, node, ProblemData, rows, out impactValue, out replacementValue, quality);
-
-        if (PruneOnlyZeroImpactNodes) {
-          if (!impactValue.IsAlmost(0.0)) continue;
-        } else if (NodeImpactThreshold < impactValue) {
-          continue;
-        }
-
-        var constantNode = (ConstantTreeNode)node.Grammar.GetSymbol("Constant").CreateTreeNode();
-        constantNode.Value = replacementValue;
-
-        ReplaceWithConstant(node, constantNode);
-        i += node.GetLength() - 1; // skip subtrees under the node that was folded
-
-        quality -= impactValue;
-      }
-      return model.SymbolicExpressionTree;
     }
 

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisExpressionTreeSimplifier.cs

@@ -287 +287 @@
         // make sum(x0..xn) / n
         var sum = original.Subtrees
-          .Select(x => GetSimplifiedTree(x))
-          .Aggregate((a, b) => MakeSum(a, b));
+          .Select(GetSimplifiedTree)
+          .Aggregate(MakeSum);
         return MakeFraction(sum, MakeConstant(original.Subtrees.Count()));
       }
@@ -299 +299 @@
         // simplify expressions x0..xn
         // make multiplication (x0 * 1/(x1 * x1 * .. * xn))
-        var simplifiedTrees = original.Subtrees.Select(GetSimplifiedTree).ToArray();
+        var first = original.GetSubtree(0);
+        var second = original.GetSubtree(1);
+        var remaining = original.Subtrees.Skip(2);
         return
-          MakeProduct(simplifiedTrees.First(), Invert(simplifiedTrees.Skip(1).Aggregate(MakeProduct)));
+          MakeProduct(GetSimplifiedTree(first), Invert(remaining.Aggregate(GetSimplifiedTree(second), (a, b) => MakeProduct(a, GetSimplifiedTree(b)))));
       }
     }
@@ -310 +312 @@
       } else {
         return original.Subtrees
-          .Select(GetSimplifiedTree).ToArray()
+          .Select(GetSimplifiedTree)
           .Aggregate(MakeProduct);
       }
@@ -321 +323 @@
         // simplify expressions x0..xn
         // make addition (x0,-x1..-xn)
-        var simplifiedTrees = original.Subtrees.Select(GetSimplifiedTree).ToArray();
-        return simplifiedTrees.Take(1)
-          .Concat(simplifiedTrees.Skip(1).Select(Negate))
-          .Aggregate(MakeSum);
+        var first = original.Subtrees.First();
+        var remaining = original.Subtrees.Skip(1);
+        return remaining.Aggregate(GetSimplifiedTree(first), (a, b) => MakeSum(a, Negate(GetSimplifiedTree(b))));
       }
     }
@@ -335 +336 @@
         // make addition (x0..xn)
         return original.Subtrees
-          .Select(GetSimplifiedTree).ToArray()
+          .Select(GetSimplifiedTree)
           .Aggregate(MakeSum);
       }
@@ -345 +346 @@
     private ISymbolicExpressionTreeNode SimplifyOr(ISymbolicExpressionTreeNode original) {
       return original.Subtrees
-        .Select(x => GetSimplifiedTree(x))
-        .Aggregate((a, b) => MakeOr(a, b));
+        .Select(GetSimplifiedTree)
+        .Aggregate(MakeOr);
     }
     private ISymbolicExpressionTreeNode SimplifyAnd(ISymbolicExpressionTreeNode original) {
       return original.Subtrees
-        .Select(x => GetSimplifiedTree(x))
-        .Aggregate((a, b) => MakeAnd(a, b));
+        .Select(GetSimplifiedTree)
+        .Aggregate(MakeAnd);
     }
     private ISymbolicExpressionTreeNode SimplifyLessThan(ISymbolicExpressionTreeNode original) {
@@ -831 +832 @@
     private void MergeVariablesInSum(ISymbolicExpressionTreeNode sum) {
       var subtrees = new List<ISymbolicExpressionTreeNode>(sum.Subtrees);
-      while (sum.Subtrees.Count() > 0) sum.RemoveSubtree(0);
+      while (sum.Subtrees.Any()) sum.RemoveSubtree(0);
       var groupedVarNodes = from node in subtrees.OfType<VariableTreeNode>()
                             let lag = (node is LaggedVariableTreeNode) ? ((LaggedVariableTreeNode)node).Lag : 0
@@ -949 +950 @@
     private void MergeVariablesAndConstantsInProduct(ISymbolicExpressionTreeNode prod) {
       var subtrees = new List<ISymbolicExpressionTreeNode>(prod.Subtrees);
-      while (prod.Subtrees.Count() > 0) prod.RemoveSubtree(0);
+      while (prod.Subtrees.Any()) prod.RemoveSubtree(0);
       var groupedVarNodes = from node in subtrees.OfType<VariableTreeNode>()
                             let lag = (node is LaggedVariableTreeNode) ? ((LaggedVariableTreeNode)node).Lag : 0
@@ -1004 +1005 @@
       } else if (IsAddition(x)) {
         // (x0 + x1 + .. + xn) * -1 => (-x0 + -x1 + .. + -xn)        
-        List<ISymbolicExpressionTreeNode> subtrees = new List<ISymbolicExpressionTreeNode>(x.Subtrees);
-        while (x.Subtrees.Count() > 0) x.RemoveSubtree(0);
+        var subtrees = new List<ISymbolicExpressionTreeNode>(x.Subtrees);
+        while (x.Subtrees.Any()) x.RemoveSubtree(0);
         foreach (var subtree in subtrees) {
           x.AddSubtree(Negate(subtree));

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisProblem.cs

@@ -321 +321 @@
       foreach (var op in operators.OfType<ISymbolicExpressionTreeCrossover>()) {
         op.ParentsParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
-        op.ChildParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
+        op.SymbolicExpressionTreeParameter.ActualName = SolutionCreator.SymbolicExpressionTreeParameter.ActualName;
       }
       foreach (var op in operators.OfType<ISymbolicExpressionTreeManipulator>()) {

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/SymbolicDataAnalysisSolutionImpactValuesCalculator.cs

@@ -41 +41 @@
 
     protected static double CalculateReplacementValue(ISymbolicExpressionTreeNode node, ISymbolicExpressionTree sourceTree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
-      Dataset dataset, IEnumerable<int> rows) {
+      IDataset dataset, IEnumerable<int> rows) {
       //optimization: constant nodes return always the same value
       ConstantTreeNode constantNode = node as ConstantTreeNode;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableConditionTreeNode.cs

@@ -74 +74 @@
       base.ResetLocalParameters(random);
       threshold = NormalDistributedRandom.NextDouble(random, Symbol.ThresholdInitializerMu, Symbol.ThresholdInitializerSigma);
+
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
+
       slope = NormalDistributedRandom.NextDouble(random, Symbol.SlopeInitializerMu, Symbol.SlopeInitializerSigma);
     }
@@ -82 +86 @@
       double x = NormalDistributedRandom.NextDouble(random, Symbol.ThresholdManipulatorMu, Symbol.ThresholdManipulatorSigma);
       threshold = threshold + x * shakingFactor;
+
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
+
       x = NormalDistributedRandom.NextDouble(random, Symbol.SlopeManipulatorMu, Symbol.SlopeManipulatorSigma);
       slope = slope + x * shakingFactor;

branches/DataAnalysis.ComplexityAnalyzer/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableTreeNode.cs

@@ -61 +61 @@
       base.ResetLocalParameters(random);
       weight = NormalDistributedRandom.NextDouble(random, Symbol.WeightMu, Symbol.WeightSigma);
+
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
     }
 
@@ -70 +73 @@
         double x = NormalDistributedRandom.NextDouble(random, Symbol.WeightManipulatorMu, Symbol.WeightManipulatorSigma);
         weight = weight + x * shakingFactor;
-      } else {        
+      } else {
         double x = NormalDistributedRandom.NextDouble(random, 1.0, Symbol.MultiplicativeWeightManipulatorSigma);
         weight = weight * x;
       }
+#pragma warning disable 612, 618
       variableName = Symbol.VariableNames.SelectRandom(random);
+#pragma warning restore 612, 618
     }