1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2010 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.Collections.Generic;
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23 | using HeuristicLab.Core;
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24 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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25 | using HeuristicLab.Data;
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26 | using System.Linq;
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27 | using System;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.GeneralSymbols;
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30 | namespace HeuristicLab.Encodings.SymbolicExpressionTreeEncoding {
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31 |
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32 |
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33 | /// <summary>
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34 | /// 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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35 | /// 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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36 | /// 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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37 | /// until a valid configuration is found.
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38 | /// </summary>
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39 | [Item("SubtreeCrossover", "An operator which performs subtree swapping crossover.")]
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40 | [StorableClass]
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41 | public class SubtreeCrossover : SymbolicExpressionTreeCrossover {
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42 | public IValueLookupParameter<PercentValue> InternalCrossoverPointProbabilityParameter {
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43 | get { return (IValueLookupParameter<PercentValue>)Parameters["InternalCrossoverPointProbability"]; }
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44 | }
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45 |
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46 | public SubtreeCrossover()
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47 | : base() {
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48 | Parameters.Add(new ValueLookupParameter<PercentValue>("InternalCrossoverPointProbability", "The probability to select an internal crossover point (instead of a leaf node).", new PercentValue(0.9)));
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49 | }
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50 |
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51 | protected override SymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionGrammar grammar,
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52 | SymbolicExpressionTree parent0, SymbolicExpressionTree parent1,
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53 | IntValue maxTreeSize, IntValue maxTreeHeight, out bool success) {
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54 | return Cross(random, grammar, parent0, parent1, InternalCrossoverPointProbabilityParameter.ActualValue.Value, maxTreeSize.Value, maxTreeHeight.Value, out success);
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55 | }
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56 |
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57 | public static SymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionGrammar grammar,
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58 | SymbolicExpressionTree parent0, SymbolicExpressionTree parent1,
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59 | double internalCrossoverPointProbability, int maxTreeSize, int maxTreeHeight, out bool success) {
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60 | // select a random crossover point in the first parent
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61 | SymbolicExpressionTreeNode crossoverPoint0;
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62 | int replacedSubtreeIndex;
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63 | SelectCrossoverPoint(random, parent0, internalCrossoverPointProbability, out crossoverPoint0, out replacedSubtreeIndex);
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64 |
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65 | // calculate the max size and height that the inserted branch can have
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66 | int maxInsertedBranchSize = maxTreeSize - (parent0.Size - crossoverPoint0.SubTrees[replacedSubtreeIndex].GetSize());
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67 | int maxInsertedBranchHeight = maxTreeHeight - GetBranchLevel(parent0.Root, crossoverPoint0);
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68 |
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69 | var allowedBranches = from branch in IterateNodes(parent1.Root)
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70 | where branch.GetSize() < maxInsertedBranchSize
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71 | where branch.GetHeight() < maxInsertedBranchHeight
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72 | where grammar.GetAllowedSymbols(crossoverPoint0.Symbol, replacedSubtreeIndex).Contains(branch.Symbol)
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73 | where IsMatchingPointType(parent0, crossoverPoint0, branch)
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74 | select branch;
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75 |
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76 | if (allowedBranches.Count() == 0) {
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77 | success = false;
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78 | return parent0;
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79 | } else {
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80 | var selectedBranch = SelectRandomBranch(random, allowedBranches, internalCrossoverPointProbability);
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81 |
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82 | // manipulate the tree of parent0 in place
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83 | // replace the branch in tree0 with the selected branch from tree1
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84 | crossoverPoint0.RemoveSubTree(replacedSubtreeIndex);
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85 | crossoverPoint0.InsertSubTree(replacedSubtreeIndex, selectedBranch);
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86 | success = true;
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87 | return parent0;
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88 | }
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89 | }
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90 |
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91 | private static bool IsMatchingPointType(SymbolicExpressionTree tree, SymbolicExpressionTreeNode parent, SymbolicExpressionTreeNode newBranch) {
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92 | var functionCalls = (from node in IterateNodes(newBranch)
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93 | let invokeNode = node as InvokeFunctionTreeNode
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94 | let argNode = node as ArgumentTreeNode
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95 | where invokeNode != null || argNode != null
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96 | let name = invokeNode != null ? invokeNode.InvokedFunctionName : "ARG" + argNode.ArgumentIndex
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97 | let argCount = invokeNode != null ? invokeNode.SubTrees.Count : 0
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98 | select new { FunctionName = name, FunctionArgumentCount = argCount }).Distinct();
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99 | var definingBranch = GetDefiningBranch(tree, parent);
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100 | if (definingBranch == null) return false;
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101 | foreach (var functionCall in functionCalls) {
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102 | if (!definingBranch.DynamicSymbols.Contains(functionCall.FunctionName) ||
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103 | definingBranch.GetDynamicSymbolArgumentCount(functionCall.FunctionName) != functionCall.FunctionArgumentCount)
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104 | return false;
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105 | }
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106 | return true;
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107 | }
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108 |
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109 | private static SymbolicExpressionTreeNode GetDefiningBranch(SymbolicExpressionTree tree, SymbolicExpressionTreeNode parent) {
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110 | foreach (var treeNode in tree.Root.SubTrees) {
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111 | if (IterateNodes(treeNode).Contains(parent)) return treeNode;
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112 | }
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113 | return null;
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114 | }
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115 |
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116 | private static void SelectCrossoverPoint(IRandom random, SymbolicExpressionTree parent0, double internalNodeProbability, out SymbolicExpressionTreeNode crossoverPoint, out int subtreeIndex) {
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117 | var crossoverPoints = from branch in IterateNodes(parent0.Root)
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118 | where branch.SubTrees.Count > 0
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119 | where !(branch.Symbol is ProgramRootSymbol)
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120 | from index in Enumerable.Range(0, branch.SubTrees.Count)
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121 | let p = new { CrossoverPoint = branch, SubtreeIndex = index, IsLeaf = branch.SubTrees[index].SubTrees.Count == 0 }
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122 | select p;
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123 | var internalCrossoverPoints = (from p in crossoverPoints
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124 | where !p.IsLeaf
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125 | select p).ToList();
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126 | var leafCrossoverPoints = (from p in crossoverPoints
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127 | where p.IsLeaf
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128 | select p).ToList();
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129 | if (internalCrossoverPoints.Count == 0) {
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130 | var selectedCrossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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131 | crossoverPoint = selectedCrossoverPoint.CrossoverPoint;
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132 | subtreeIndex = selectedCrossoverPoint.SubtreeIndex;
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133 | } else if (leafCrossoverPoints.Count == 0) {
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134 | var selectedCrossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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135 | crossoverPoint = selectedCrossoverPoint.CrossoverPoint;
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136 | subtreeIndex = selectedCrossoverPoint.SubtreeIndex;
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137 | } else if (random.NextDouble() < internalNodeProbability && internalCrossoverPoints.Count > 0) {
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138 | // select internal crossover point or leaf
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139 | var selectedCrossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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140 | crossoverPoint = selectedCrossoverPoint.CrossoverPoint;
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141 | subtreeIndex = selectedCrossoverPoint.SubtreeIndex;
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142 | } else {
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143 | var selectedCrossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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144 | crossoverPoint = selectedCrossoverPoint.CrossoverPoint;
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145 | subtreeIndex = selectedCrossoverPoint.SubtreeIndex;
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146 | }
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147 | }
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148 |
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149 | private static SymbolicExpressionTreeNode SelectRandomBranch(IRandom random, IEnumerable<SymbolicExpressionTreeNode> branches, double internalNodeProbability) {
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150 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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151 | var groupedBranches = from branch in branches
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152 | group branch by branch.SubTrees.Count into g
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153 | select g;
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154 | var allowedInternalBranches = (from g in groupedBranches
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155 | where g.Key > 0
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156 | from branch in g
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157 | select branch).ToList();
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158 | var allowedLeafBranches = (from g in groupedBranches
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159 | where g.Key == 0
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160 | from leaf in g
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161 | select leaf).ToList();
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162 | if (allowedInternalBranches.Count == 0) {
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163 | return allowedLeafBranches[random.Next(allowedLeafBranches.Count)];
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164 | } else if (allowedLeafBranches.Count == 0) {
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165 | return allowedInternalBranches[random.Next(allowedInternalBranches.Count)];
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166 | } else if (random.NextDouble() < internalNodeProbability) {
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167 | // when leaf and internal nodes are possible then choose either a leaf or internal node with internalNodeProbability
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168 | return allowedInternalBranches[random.Next(allowedInternalBranches.Count)];
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169 | } else {
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170 | return allowedLeafBranches[random.Next(allowedLeafBranches.Count)];
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171 | }
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172 | }
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173 |
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174 | private static IEnumerable<SymbolicExpressionTreeNode> IterateNodes(SymbolicExpressionTreeNode root) {
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175 | foreach (var subTree in root.SubTrees) {
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176 | foreach (var branch in IterateNodes(subTree)) {
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177 | yield return branch;
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178 | }
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179 | }
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180 | yield return root;
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181 | }
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182 |
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183 | private static int GetBranchLevel(SymbolicExpressionTreeNode root, SymbolicExpressionTreeNode point) {
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184 | if (root == point) return 0;
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185 | foreach (var subtree in root.SubTrees) {
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186 | int branchLevel = GetBranchLevel(subtree, point);
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187 | if (branchLevel < int.MaxValue) return 1 + branchLevel;
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188 | }
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189 | return int.MaxValue;
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190 | }
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191 | }
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192 | }
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