Index: branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj
===================================================================
--- branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj	(revision 16476)
+++ branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj	(revision 16529)
@@ -127,4 +127,5 @@
     <Compile Include="MultiObjective\PearsonRSquaredNestedTreeSizeEvaluator.cs" />
     <Compile Include="MultiObjective\PearsonRSquaredNumberOfVariablesEvaluator.cs" />
+    <Compile Include="MultiObjective\PearsonRSquaredAverageSimilarityEvaluator.cs" />
     <Compile Include="MultiObjective\PearsonRSquaredTreeComplexityEvaluator.cs" />
     <Compile Include="MultiObjective\SymbolicRegressionMultiObjectiveValidationBestSolutionAnalyzer.cs" />
Index: branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredAverageSimilarityEvaluator.cs
===================================================================
--- branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredAverageSimilarityEvaluator.cs	(revision 16529)
+++ branches/2520_PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/PearsonRSquaredAverageSimilarityEvaluator.cs	(revision 16529)
@@ -0,0 +1,134 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2018 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 <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Diagnostics;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
+  [Item("Pearson R² & Average Similarity Evaluator", "Calculates the Pearson R² and the average similarity of a symbolic regression solution candidate.")]
+  [StorableClass]
+  public class PearsonRSquaredAverageSimilarityEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
+    private const string StrictSimilarityParameterName = "StrictSimilarity";
+
+    private readonly object locker = new object();
+
+    public IFixedValueParameter<BoolValue> StrictSimilarityParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters[StrictSimilarityParameterName]; }
+    }
+
+    public bool StrictSimilarity {
+      get { return StrictSimilarityParameter.Value.Value; }
+    }
+
+    [StorableConstructor]
+    protected PearsonRSquaredAverageSimilarityEvaluator(bool deserializing) : base(deserializing) { }
+    protected PearsonRSquaredAverageSimilarityEvaluator(PearsonRSquaredAverageSimilarityEvaluator original, Cloner cloner)
+      : base(original, cloner) {
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new PearsonRSquaredAverageSimilarityEvaluator(this, cloner);
+    }
+
+    public PearsonRSquaredAverageSimilarityEvaluator() : base() {
+      Parameters.Add(new FixedValueParameter<BoolValue>(StrictSimilarityParameterName, "Use strict similarity calculation.", new BoolValue(false)));
+    }
+
+    public override IEnumerable<bool> Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize model complexity 
+
+    public override IOperation InstrumentedApply() {
+      IEnumerable<int> rows = GenerateRowsToEvaluate();
+      var solution = SymbolicExpressionTreeParameter.ActualValue;
+      var problemData = ProblemDataParameter.ActualValue;
+      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
+      var estimationLimits = EstimationLimitsParameter.ActualValue;
+      var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
+
+      if (UseConstantOptimization) {
+        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper);
+      }
+      double[] qualities = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling, DecimalPlaces);
+      QualitiesParameter.ActualValue = new DoubleArray(qualities);
+      return base.InstrumentedApply();
+    }
+
+    public double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows, bool applyLinearScaling, int decimalPlaces) {
+      double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
+      if (decimalPlaces >= 0)
+        r2 = Math.Round(r2, decimalPlaces);
+
+      var variables = ExecutionContext.Scope.Variables;
+      if (!variables.ContainsKey("AverageSimilarity")) {
+        lock (locker) {
+          CalculateAverageSimilarities(ExecutionContext.Scope.Parent.SubScopes.Where(x => x.Variables.ContainsKey("SymbolicExpressionTree")).ToArray(), StrictSimilarity);
+
+        }
+      }
+
+      double avgSim = ((DoubleValue)variables["AverageSimilarity"].Value).Value;
+      return new double[2] { r2, avgSim };
+    }
+
+    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
+      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
+      EstimationLimitsParameter.ExecutionContext = context;
+      ApplyLinearScalingParameter.ExecutionContext = context;
+      // DecimalPlaces parameter is a FixedValueParameter and doesn't need the context.
+
+      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
+
+      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
+      EstimationLimitsParameter.ExecutionContext = null;
+      ApplyLinearScalingParameter.ExecutionContext = null;
+
+      return quality;
+    }
+
+    private readonly Stopwatch sw = new Stopwatch();
+    public void CalculateAverageSimilarities(IScope[] treeScopes, bool strict) {
+      var trees = treeScopes.Select(x => (ISymbolicExpressionTree)x.Variables["SymbolicExpressionTree"].Value).ToArray();
+      var similarityMatrix = SymbolicExpressionTreeHash.ComputeSimilarityMatrix(trees, simplify: false, strict: strict);
+
+      for (int i = 0; i < treeScopes.Length; ++i) {
+        var scope = treeScopes[i];
+        var avgSimilarity = 0d;
+        for (int j = 0; j < trees.Length; ++j) {
+          if (i == j) continue;
+          avgSimilarity += similarityMatrix[i, j];
+        }
+        avgSimilarity /= trees.Length - 1;
+
+        if (scope.Variables.ContainsKey("AverageSimilarity")) {
+          ((DoubleValue)scope.Variables["AverageSimilarity"].Value).Value = avgSimilarity;
+        } else {
+          scope.Variables.Add(new Core.Variable("AverageSimilarity", new DoubleValue(avgSimilarity)));
+        }
+      }
+    }
+  }
+}
