1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2011 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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25 | using HeuristicLab.Common;
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26 | using HeuristicLab.Core;
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27 | using HeuristicLab.Data;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 |
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31 | namespace HeuristicLab.Encodings.SymbolicExpressionTreeEncoding {
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32 | /// <summary>
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33 | /// Takes two parent individuals P0 and P1 each. Selects a random node N0 of P0 and a random node N1 of P1.
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34 | /// And replaces the branch with root0 N0 in P0 with N1 from P1 if the tree-size limits are not violated.
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35 | /// When recombination with N0 and N1 would create a tree that is too large or invalid the operator randomly selects new N0 and N1
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36 | /// until a valid configuration is found.
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37 | /// </summary>
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38 | [Item("SubtreeCrossover", "An operator which performs subtree swapping crossover.")]
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39 | [StorableClass]
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40 | public sealed class SubtreeCrossover : SymbolicExpressionTreeCrossover, ISymbolicExpressionTreeSizeConstraintOperator {
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41 | private const string InternalCrossoverPointProbabilityParameterName = "InternalCrossoverPointProbability";
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42 | private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
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43 | private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
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44 | #region Parameter Properties
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45 | public IValueLookupParameter<PercentValue> InternalCrossoverPointProbabilityParameter {
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46 | get { return (IValueLookupParameter<PercentValue>)Parameters[InternalCrossoverPointProbabilityParameterName]; }
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47 | }
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48 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
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49 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
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50 | }
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51 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeDepthParameter {
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52 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeDepthParameterName]; }
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53 | }
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54 | #endregion
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55 | #region Properties
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56 | public PercentValue InternalCrossoverPointProbability {
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57 | get { return InternalCrossoverPointProbabilityParameter.ActualValue; }
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58 | }
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59 | public IntValue MaximumSymbolicExpressionTreeLength {
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60 | get { return MaximumSymbolicExpressionTreeLengthParameter.ActualValue; }
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61 | }
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62 | public IntValue MaximumSymbolicExpressionTreeDepth {
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63 | get { return MaximumSymbolicExpressionTreeDepthParameter.ActualValue; }
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64 | }
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65 | #endregion
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66 | [StorableConstructor]
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67 | private SubtreeCrossover(bool deserializing) : base(deserializing) { }
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68 | private SubtreeCrossover(SubtreeCrossover original, Cloner cloner) : base(original, cloner) { }
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69 | public SubtreeCrossover()
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70 | : base() {
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71 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, "The maximal length (number of nodes) of the symbolic expression tree."));
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72 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeDepthParameterName, "The maximal depth of the symbolic expression tree (a tree with one node has depth = 0)."));
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73 | Parameters.Add(new ValueLookupParameter<PercentValue>(InternalCrossoverPointProbabilityParameterName, "The probability to select an internal crossover point (instead of a leaf node).", new PercentValue(0.9)));
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74 | }
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75 |
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76 | public override IDeepCloneable Clone(Cloner cloner) {
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77 | return new SubtreeCrossover(this, cloner);
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78 | }
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79 |
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80 | protected override ISymbolicExpressionTree Cross(IRandom random,
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81 | ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
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82 | return Cross(random, parent0, parent1, InternalCrossoverPointProbability.Value,
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83 | MaximumSymbolicExpressionTreeLength.Value, MaximumSymbolicExpressionTreeDepth.Value);
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84 | }
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85 |
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86 | public static ISymbolicExpressionTree Cross(IRandom random,
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87 | ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
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88 | double internalCrossoverPointProbability, int maxTreeLength, int maxTreeDepth) {
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89 | // select a random crossover point in the first parent
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90 | CutPoint crossoverPoint0;
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91 | SelectCrossoverPoint(random, parent0, internalCrossoverPointProbability, maxTreeLength, maxTreeDepth, out crossoverPoint0);
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92 |
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93 | int childLength = crossoverPoint0.Child != null ? crossoverPoint0.Child.GetLength() : 0;
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94 | // calculate the max length and depth that the inserted branch can have
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95 | int maxInsertedBranchLength = maxTreeLength - (parent0.Length - childLength);
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96 | int maxInsertedBranchDepth = maxTreeDepth - GetBranchLevel(parent0.Root, crossoverPoint0.Parent);
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97 |
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98 | List<ISymbolicExpressionTreeNode> allowedBranches = new List<ISymbolicExpressionTreeNode>();
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99 | parent1.Root.ForEachNodePostfix((n) => {
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100 | if (!n.Symbol.Fixed &&
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101 | n.GetLength() <= maxInsertedBranchLength &&
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102 | n.GetDepth() <= maxInsertedBranchDepth &&
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103 | IsMatchingPointType(crossoverPoint0, n))
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104 | allowedBranches.Add(n);
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105 | });
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106 | // empty branch
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107 | if (IsMatchingPointType(crossoverPoint0, null)) allowedBranches.Add(null);
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108 |
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109 | if (allowedBranches.Count == 0) {
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110 | return parent0;
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111 | } else {
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112 | var selectedBranch = SelectRandomBranch(random, allowedBranches, internalCrossoverPointProbability);
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113 |
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114 | if (crossoverPoint0.Child != null) {
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115 | // manipulate the tree of parent0 in place
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116 | // replace the branch in tree0 with the selected branch from tree1
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117 | crossoverPoint0.Parent.RemoveSubtree(crossoverPoint0.ChildIndex);
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118 | if (selectedBranch != null) {
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119 | crossoverPoint0.Parent.InsertSubtree(crossoverPoint0.ChildIndex, selectedBranch);
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120 | }
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121 | } else {
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122 | // child is null (additional child should be added under the parent)
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123 | if (selectedBranch != null) {
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124 | crossoverPoint0.Parent.AddSubtree(selectedBranch);
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125 | }
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126 | }
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127 | return parent0;
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128 | }
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129 | }
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130 |
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131 | private static bool IsMatchingPointType(CutPoint cutPoint, ISymbolicExpressionTreeNode newChild) {
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132 | var parent = cutPoint.Parent;
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133 | if (newChild == null) {
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134 | // make sure that one subtree can be removed and that only the last subtree is removed
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135 | return parent.Grammar.GetMinimumSubtreeCount(parent.Symbol) < parent.SubtreeCount &&
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136 | cutPoint.ChildIndex == parent.SubtreeCount - 1;
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137 | } else {
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138 | // check syntax constraints of direct parent - child relation
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139 | if (!parent.Grammar.ContainsSymbol(newChild.Symbol) ||
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140 | !parent.Grammar.IsAllowedChildSymbol(parent.Symbol, newChild.Symbol, cutPoint.ChildIndex)) return false;
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141 |
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142 | bool result = true;
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143 | // check point type for the whole branch
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144 | newChild.ForEachNodePostfix((n) => {
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145 | result =
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146 | result &&
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147 | parent.Grammar.ContainsSymbol(n.Symbol) &&
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148 | n.SubtreeCount >= parent.Grammar.GetMinimumSubtreeCount(n.Symbol) &&
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149 | n.SubtreeCount <= parent.Grammar.GetMaximumSubtreeCount(n.Symbol);
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150 | });
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151 | return result;
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152 | }
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153 | }
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154 |
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155 | private static void SelectCrossoverPoint(IRandom random, ISymbolicExpressionTree parent0, double internalNodeProbability, int maxBranchLength, int maxBranchDepth, out CutPoint crossoverPoint) {
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156 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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157 | List<CutPoint> internalCrossoverPoints = new List<CutPoint>();
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158 | List<CutPoint> leafCrossoverPoints = new List<CutPoint>();
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159 | parent0.Root.ForEachNodePostfix((n) => {
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160 | if (!n.Symbol.Fixed && n.Subtrees.Any() && n != parent0.Root) {
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161 | foreach (var child in n.Subtrees) {
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162 | if (child.GetLength() <= maxBranchLength &&
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163 | child.GetDepth() <= maxBranchDepth) {
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164 | if (child.Subtrees.Any())
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165 | internalCrossoverPoints.Add(new CutPoint(n, child));
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166 | else
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167 | leafCrossoverPoints.Add(new CutPoint(n, child));
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168 | }
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169 | }
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170 | // add one additional extension point if the number of sub trees for the symbol is not full
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171 | if (n.SubtreeCount < n.Grammar.GetMaximumSubtreeCount(n.Symbol)) {
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172 | // empty extension point
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173 | internalCrossoverPoints.Add(new CutPoint(n, n.SubtreeCount));
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174 | }
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175 | }
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176 | });
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177 |
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178 | if (random.NextDouble() < internalNodeProbability) {
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179 | // select from internal node if possible
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180 | if (internalCrossoverPoints.Count > 0) {
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181 | // select internal crossover point or leaf
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182 | crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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183 | } else {
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184 | // otherwise select external node
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185 | crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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186 | }
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187 | } else if (leafCrossoverPoints.Count > 0) {
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188 | // select from leaf crossover point if possible
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189 | crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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190 | } else {
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191 | // otherwise select internal crossover point
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192 | crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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193 | }
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194 | }
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195 |
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196 | private static ISymbolicExpressionTreeNode SelectRandomBranch(IRandom random, IEnumerable<ISymbolicExpressionTreeNode> branches, double internalNodeProbability) {
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197 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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198 | List<ISymbolicExpressionTreeNode> allowedInternalBranches;
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199 | List<ISymbolicExpressionTreeNode> allowedLeafBranches;
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200 | if (random.NextDouble() < internalNodeProbability) {
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201 | // select internal node if possible
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202 | allowedInternalBranches = (from branch in branches
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203 | where branch != null && branch.Subtrees.Any()
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204 | select branch).ToList();
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205 | if (allowedInternalBranches.Count > 0) {
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206 | return allowedInternalBranches.SelectRandom(random);
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207 | } else {
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208 | // no internal nodes allowed => select leaf nodes
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209 | allowedLeafBranches = (from branch in branches
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210 | where branch == null || !branch.Subtrees.Any()
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211 | select branch).ToList();
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212 | return allowedLeafBranches.SelectRandom(random);
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213 | }
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214 | } else {
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215 | // select leaf node if possible
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216 | allowedLeafBranches = (from branch in branches
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217 | where branch == null || !branch.Subtrees.Any()
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218 | select branch).ToList();
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219 | if (allowedLeafBranches.Count > 0) {
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220 | return allowedLeafBranches.SelectRandom(random);
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221 | } else {
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222 | allowedInternalBranches = (from branch in branches
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223 | where branch != null && branch.Subtrees.Any()
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224 | select branch).ToList();
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225 | return allowedInternalBranches.SelectRandom(random);
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226 | }
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227 | }
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228 | }
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229 |
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230 | private static int GetBranchLevel(ISymbolicExpressionTreeNode root, ISymbolicExpressionTreeNode point) {
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231 | if (root == point) return 0;
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232 | foreach (var subtree in root.Subtrees) {
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233 | int branchLevel = GetBranchLevel(subtree, point);
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234 | if (branchLevel < int.MaxValue) return 1 + branchLevel;
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235 | }
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236 | return int.MaxValue;
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237 | }
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238 | }
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239 | }
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