1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2013 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.Common;
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24 | using HeuristicLab.Core;
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25 | using HeuristicLab.Data;
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26 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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29 |
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30 | namespace HeuristicLab.Problems.GrammaticalEvolution {
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31 |
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32 | /// <summary>
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33 | /// Position Independent (PI) Grammatical Evolution Mapper
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34 | /// -----------------------------------------------------------------------------------
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35 | /// Standard GE mappers:
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36 | /// Rule = Codon Value % Number Of Rules
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37 | ///
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38 | /// 𝜋GE:
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39 | /// 𝜋GE codons consist of (nont, rule) tuples, where nont may be one value from an "order"
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40 | /// integer vector and rule may be one value from a "content" integer vector.
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41 | ///
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42 | /// Order: NT = nont % Num. NT ... determines, which non-terminal to expand next
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43 | /// Content: Rule = rule % Num. Rules ... rule determination as with standard GE mappers
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44 | ///
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45 | /// Four mutation and crossover strategies possible:
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46 | /// * Order-only: only "order" vector is modified, "content" vector is fixed (1:0),
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47 | /// worst result according to [2]
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48 | /// * Content-only: only "content" vector is modified, "order" vector is fixed (0:1),
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49 | /// best result according to [2]
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50 | /// * 𝜋GE: genetic operators are applied equally (1:1),
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51 | /// * Content:Order: genetic operators are applied unequally (e.g. 2:1 or 1:2),
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52 | ///
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53 | /// Here, the "content-only" strategy is implemented, as it is the best solution according to [2]
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54 | /// and it does not require much to change in the problem and evaluator classes.
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55 | ///
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56 | /// </summary>
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57 | /// <remarks>
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58 | /// Described in
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59 | ///
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60 | /// [1] Michael O’Neill et al. 𝜋Grammatical Evolution. In: GECCO 2004.
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61 | /// Vol. 3103. LNCS. Heidelberg: Springer-Verlag Berlin, 2004, pp. 617–629.
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62 | /// url: http://dynamics.org/Altenberg/UH_ICS/EC_REFS/GP_REFS/GECCO/2004/31030617.pdf.
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63 | ///
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64 | /// [2] David Fagan et al. Investigating Mapping Order in πGE. IEEE, 2010
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65 | /// url: http://ncra.ucd.ie/papers/pigeWCCI2010.pdf
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66 | /// </remarks>
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67 | [Item("PIGEMapper", "Position Independent (PI) Grammatical Evolution Mapper")]
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68 | [StorableClass]
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69 | public class PIGEMapper : GenotypeToPhenotypeMapper {
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70 |
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71 | private IntegerVector nontVector;
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72 |
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73 | public IntegerVector NontVector {
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74 | get { return nontVector; }
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75 | set { nontVector = value; }
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76 | }
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77 |
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78 | private IntegerVector GetNontVector(IRandom random, IntMatrix bounds, int length) {
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79 | IntegerVector v = new IntegerVector(length);
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80 | v.Randomize(random, bounds);
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81 | return v;
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82 | }
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83 |
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84 | [StorableConstructor]
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85 | protected PIGEMapper(bool deserializing) : base(deserializing) { }
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86 | protected PIGEMapper(PIGEMapper original, Cloner cloner) : base(original, cloner) { }
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87 | public PIGEMapper() : base() { }
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88 |
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89 | public override IDeepCloneable Clone(Cloner cloner) {
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90 | return new PIGEMapper(this, cloner);
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91 | }
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92 |
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93 |
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94 | /// <summary>
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95 | /// Maps a genotype (an integer vector) to a phenotype (a symbolic expression tree).
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96 | /// PIGE approach.
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97 | /// </summary>
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98 | /// <param name="random">random number generator</param>
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99 | /// <param name="bounds">integer number range for genomes (codons) of the nont vector</param>
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100 | /// <param name="length">length of the nont vector to create</param>
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101 | /// <param name="grammar">grammar definition</param>
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102 | /// <param name="genotype">integer vector, which should be mapped to a tree</param>
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103 | /// <returns>phenotype (a symbolic expression tree)</returns>
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104 | public override SymbolicExpressionTree Map(IRandom random, IntMatrix bounds, int length,
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105 | ISymbolicExpressionGrammar grammar,
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106 | IntegerVector genotype) {
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107 |
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108 | SymbolicExpressionTree tree = new SymbolicExpressionTree();
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109 | var rootNode = (SymbolicExpressionTreeTopLevelNode)grammar.ProgramRootSymbol.CreateTreeNode();
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110 | var startNode = (SymbolicExpressionTreeTopLevelNode)grammar.StartSymbol.CreateTreeNode();
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111 | rootNode.AddSubtree(startNode);
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112 | tree.Root = rootNode;
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113 |
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114 | if (NontVector == null) {
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115 | NontVector = GetNontVector(random, bounds, length);
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116 | }
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117 |
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118 | MapPIGEIteratively(startNode, genotype, grammar,
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119 | genotype.Length, random);
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120 |
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121 | return tree;
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122 | }
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123 |
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124 |
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125 | /// <summary>
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126 | /// Genotype-to-Phenotype mapper (iterative 𝜋GE approach, using a list of not expanded nonTerminals).
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127 | /// </summary>
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128 | /// <param name="startNode">first node of the tree with arity 1</param>
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129 | /// <param name="genotype">integer vector, which should be mapped to a tree</param>
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130 | /// <param name="grammar">grammar to determine the allowed child symbols for each node</param>
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131 | /// <param name="maxSubtreeCount">maximum allowed subtrees (= number of used genomes)</param>
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132 | /// <param name="random">random number generator</param>
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133 | private void MapPIGEIteratively(ISymbolicExpressionTreeNode startNode,
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134 | IntegerVector genotype,
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135 | ISymbolicExpressionGrammar grammar,
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136 | int maxSubtreeCount, IRandom random) {
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137 |
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138 | List<ISymbolicExpressionTreeNode> nonTerminals = new List<ISymbolicExpressionTreeNode>();
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139 |
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140 | int genotypeIndex = 0;
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141 | nonTerminals.Add(startNode);
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142 |
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143 | while (nonTerminals.Count > 0) {
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144 |
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145 | if (genotypeIndex >= maxSubtreeCount) {
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146 | // if all genomes were used, only add terminal nodes to the remaining subtrees
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147 | ISymbolicExpressionTreeNode current = nonTerminals[0];
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148 | nonTerminals.RemoveAt(0);
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149 | current.AddSubtree(GetRandomTerminalNode(current, grammar, random));
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150 | } else {
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151 | // Order: NT = nont % Num. NT
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152 | int nt = NontVector[genotypeIndex] % nonTerminals.Count;
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153 | ISymbolicExpressionTreeNode current = nonTerminals[nt];
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154 | nonTerminals.RemoveAt(nt);
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155 |
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156 | // Content: Rule = rule % Num. Rules
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157 | ISymbolicExpressionTreeNode newNode = GetNewChildNode(current, genotype, grammar, genotypeIndex, random);
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158 | int arity = SampleArity(random, newNode, grammar);
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159 |
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160 | current.AddSubtree(newNode);
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161 | genotypeIndex++;
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162 | // new node has subtrees, so add "arity" number of copies of this node to the nonTerminals list
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163 | for (int i = 0; i < arity; ++i) {
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164 | nonTerminals.Add(newNode);
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165 | }
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166 | }
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167 | }
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168 | }
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169 | }
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170 | } |
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