[7476] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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[7481] | 25 | using HeuristicLab.Common;
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[7476] | 26 | using HeuristicLab.Core;
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[7481] | 27 | using HeuristicLab.Data;
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[7476] | 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 29 | using HeuristicLab.Parameters;
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| 30 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 31 | using HeuristicLab.Random;
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| 32 |
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| 33 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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| 34 | public abstract class SymbolicDataAnalysisExpressionCrossover<T> : SymbolicExpressionTreeCrossover, ISymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
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| 35 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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| 36 | private const string ProblemDataParameterName = "ProblemData";
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| 37 | private const string EvaluatorParameterName = "Evaluator";
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[7506] | 38 | private const string EvaluationPartitionParameterName = "EvaluationPartition";
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[7476] | 39 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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| 40 | private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
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| 41 | private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
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| 42 |
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| 43 | #region Parameter properties
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| 44 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 45 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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| 46 | }
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| 47 | public IValueLookupParameter<T> ProblemDataParameter {
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| 48 | get { return (IValueLookupParameter<T>)Parameters[ProblemDataParameterName]; }
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| 49 | }
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| 50 | public ILookupParameter<ISymbolicDataAnalysisSingleObjectiveEvaluator<T>> EvaluatorParameter {
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| 51 | get { return (ILookupParameter<ISymbolicDataAnalysisSingleObjectiveEvaluator<T>>)Parameters[EvaluatorParameterName]; }
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| 52 | }
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[7506] | 53 | public IValueLookupParameter<IntRange> EvaluationPartitionParameter {
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| 54 | get { return (IValueLookupParameter<IntRange>)Parameters[EvaluationPartitionParameterName]; }
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[7476] | 55 | }
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| 56 | public IValueLookupParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 57 | get { return (IValueLookupParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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| 58 | }
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| 59 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
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| 60 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
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| 61 | }
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| 62 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeDepthParameter {
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| 63 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeDepthParameterName]; }
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| 64 | }
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| 65 | #endregion
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| 66 |
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| 67 | #region Properties
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| 68 | public IntValue MaximumSymbolicExpressionTreeLength {
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| 69 | get { return MaximumSymbolicExpressionTreeLengthParameter.ActualValue; }
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| 70 | }
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| 71 | public IntValue MaximumSymbolicExpressionTreeDepth {
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| 72 | get { return MaximumSymbolicExpressionTreeDepthParameter.ActualValue; }
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| 73 | }
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| 74 | #endregion
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| 75 |
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| 76 | [StorableConstructor]
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| 77 | protected SymbolicDataAnalysisExpressionCrossover(bool deserializing) : base(deserializing) { }
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| 78 | protected SymbolicDataAnalysisExpressionCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
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| 79 | : base(original, cloner) {
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| 80 | }
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| 81 | public SymbolicDataAnalysisExpressionCrossover()
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| 82 | : base() {
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| 83 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
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| 84 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisSingleObjectiveEvaluator<T>>(EvaluatorParameterName, "The single objective solution evaluator"));
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| 85 | Parameters.Add(new ValueLookupParameter<T>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
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[7506] | 86 | Parameters.Add(new ValueLookupParameter<IntRange>(EvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
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[7476] | 87 | Parameters.Add(new ValueLookupParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index."));
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| 88 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeDepthParameterName, "The maximum tree depth."));
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| 89 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, "The maximum tree length."));
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| 90 |
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| 91 | EvaluatorParameter.Hidden = true;
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[7506] | 92 | EvaluationPartitionParameter.Hidden = true;
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[7476] | 93 | SymbolicDataAnalysisTreeInterpreterParameter.Hidden = true;
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| 94 | ProblemDataParameter.Hidden = true;
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| 95 | RelativeNumberOfEvaluatedSamplesParameter.Hidden = true;
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| 96 | }
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| 97 |
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| 98 | /// <summary>
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| 99 | /// Creates a SymbolicExpressionTreeNode reusing the root and start symbols (since they are expensive to create).
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| 100 | /// </summary>
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| 101 | /// <param name="random"></param>
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| 102 | /// <param name="node"></param>
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| 103 | /// <param name="rootSymbol"></param>
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| 104 | /// <param name="startSymbol"></param>
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| 105 | /// <returns></returns>
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| 106 | protected static ISymbolicExpressionTree CreateTreeFromNode(IRandom random, ISymbolicExpressionTreeNode node, ISymbol rootSymbol, ISymbol startSymbol) {
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| 107 | var rootNode = new SymbolicExpressionTreeTopLevelNode(rootSymbol);
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| 108 | if (rootNode.HasLocalParameters) rootNode.ResetLocalParameters(random);
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| 109 |
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| 110 | var startNode = new SymbolicExpressionTreeTopLevelNode(startSymbol);
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| 111 | if (startNode.HasLocalParameters) startNode.ResetLocalParameters(random);
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| 112 |
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| 113 | startNode.AddSubtree(node);
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| 114 | rootNode.AddSubtree(startNode);
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| 115 |
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| 116 | return new SymbolicExpressionTree(rootNode);
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| 117 | }
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| 118 |
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| 119 | protected IEnumerable<int> GenerateRowsToEvaluate() {
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| 120 | return GenerateRowsToEvaluate(RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value);
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| 121 | }
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| 122 |
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| 123 | protected IEnumerable<int> GenerateRowsToEvaluate(double percentageOfRows) {
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| 124 | IEnumerable<int> rows;
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[7506] | 125 | int samplesStart = EvaluationPartitionParameter.ActualValue.Start;
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| 126 | int samplesEnd = EvaluationPartitionParameter.ActualValue.End;
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[7476] | 127 | int testPartitionStart = ProblemDataParameter.ActualValue.TestPartition.Start;
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| 128 | int testPartitionEnd = ProblemDataParameter.ActualValue.TestPartition.End;
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| 129 |
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| 130 | if (samplesEnd < samplesStart) throw new ArgumentException("Start value is larger than end value.");
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| 131 |
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| 132 | if (percentageOfRows.IsAlmost(1.0))
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| 133 | rows = Enumerable.Range(samplesStart, samplesEnd - samplesStart);
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| 134 | else {
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| 135 | int seed = RandomParameter.ActualValue.Next();
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| 136 | int count = (int)((samplesEnd - samplesStart) * percentageOfRows);
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| 137 | if (count == 0) count = 1;
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| 138 | rows = RandomEnumerable.SampleRandomNumbers(seed, samplesStart, samplesEnd, count);
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| 139 | }
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| 140 |
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| 141 | return rows.Where(i => i < testPartitionStart || testPartitionEnd <= i);
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| 142 | }
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[7494] | 143 |
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| 144 | protected static void Swap(CutPoint crossoverPoint, ISymbolicExpressionTreeNode selectedBranch) {
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| 145 | if (crossoverPoint.Child != null) {
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| 146 | // manipulate the tree of parent0 in place
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| 147 | // replace the branch in tree0 with the selected branch from tree1
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| 148 | crossoverPoint.Parent.RemoveSubtree(crossoverPoint.ChildIndex);
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| 149 | if (selectedBranch != null) {
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| 150 | crossoverPoint.Parent.InsertSubtree(crossoverPoint.ChildIndex, selectedBranch);
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| 151 | }
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| 152 | } else {
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| 153 | // child is null (additional child should be added under the parent)
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| 154 | if (selectedBranch != null) {
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| 155 | crossoverPoint.Parent.AddSubtree(selectedBranch);
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| 156 | }
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| 157 | }
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| 158 | }
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[7476] | 159 | }
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| 160 | }
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