1 | /* |
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2 | Copyright 2006 by Sean Luke |
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3 | Licensed under the Academic Free License version 3.0 |
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4 | See the file "LICENSE" for more information |
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5 | */ |
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6 | |
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7 | package ec.vector.breed; |
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8 | |
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9 | import ec.vector.*; |
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10 | import ec.*; |
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11 | import ec.util.Parameter; |
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12 | |
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13 | |
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14 | /* |
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15 | * MultipleVectorCrossoverPipeline.java |
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16 | * |
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17 | * Created: Thu May 14 2009 |
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18 | * By: Beenish Jamil |
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19 | */ |
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20 | |
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21 | |
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22 | /** |
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23 | * |
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24 | MultipleVectorCrossoverPipeline is a BreedingPipeline which implements a uniform |
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25 | (any point) crossover between multiple vectors. It is intended to be used with |
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26 | three or more vectors. It takes n parent individuals and returns n crossed over |
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27 | individuals. The number of parents and consequently children is specified by the |
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28 | number of sources parameter. |
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29 | <p>The standard vector crossover probability is used for this crossover type. |
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30 | <br> <i> Note</i> : It is necessary to set the crossover-type parameter to 'any' |
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31 | in order to use this pipeline. |
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32 | |
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33 | |
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34 | <p><b>Typical Number of Individuals Produced Per <tt>produce(...)</tt> call</b><br> |
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35 | number of parents |
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36 | |
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37 | <p><b>Number of Sources</b><br> |
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38 | variable (generally 3 or more) |
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39 | |
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40 | |
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41 | <p><b>Default Base</b><br> |
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42 | vector.multixover |
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43 | */ |
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44 | |
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45 | // This class is MUCH MUCH longer than it need be. We could just do it by using |
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46 | // ECJ's generic split and join operations, but only rely on that in the default |
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47 | // case, and instead use faster per-array operations. |
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48 | |
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49 | |
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50 | public class MultipleVectorCrossoverPipeline extends BreedingPipeline { |
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51 | |
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52 | /** default base */ |
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53 | public static final String P_CROSSOVER = "multixover"; |
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54 | |
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55 | /** Temporary holding place for parents */ |
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56 | VectorIndividual[] parents; |
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57 | |
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58 | public Parameter defaultBase() { return VectorDefaults.base().push(P_CROSSOVER); } |
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59 | |
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60 | /** Returns the number of parents */ |
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61 | public int numSources() { return DYNAMIC_SOURCES;} |
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62 | |
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63 | public Object clone() |
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64 | { |
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65 | MultipleVectorCrossoverPipeline c = (MultipleVectorCrossoverPipeline)(super.clone()); |
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66 | |
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67 | // deep-cloned stuff |
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68 | c.parents = (VectorIndividual[]) parents.clone(); |
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69 | |
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70 | return c; |
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71 | } |
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72 | |
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73 | public void setup(final EvolutionState state, final Parameter base) |
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74 | { |
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75 | super.setup(state,base); |
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76 | parents = new VectorIndividual[sources.length]; |
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77 | } |
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78 | |
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79 | /** |
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80 | * Returns the minimum number of children that are produced per crossover |
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81 | */ |
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82 | public int typicalIndsProduced() |
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83 | { |
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84 | return minChildProduction()*sources.length; // minChild is always 1 |
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85 | } |
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86 | |
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87 | |
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88 | public int produce(final int min, |
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89 | final int max, |
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90 | final int start, |
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91 | final int subpopulation, |
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92 | final Individual[] inds, |
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93 | final EvolutionState state, |
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94 | final int thread) |
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95 | |
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96 | { |
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97 | |
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98 | // how many individuals should we make? |
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99 | int n = typicalIndsProduced(); |
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100 | if (n < min) n = min; |
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101 | if (n > max) n = max; |
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102 | |
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103 | |
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104 | // should we bother? |
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105 | if (!state.random[thread].nextBoolean(likelihood)) |
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106 | return reproduce(n, start, subpopulation, inds, state, thread, true); // DO produce children from source -- we've not done so already |
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107 | |
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108 | |
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109 | if(inds[0] instanceof BitVectorIndividual) |
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110 | n = multipleBitVectorCrossover(min, max, start, subpopulation, // redundant reassignment |
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111 | inds, state, thread); |
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112 | |
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113 | else if(inds[0] instanceof ByteVectorIndividual) |
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114 | n = multipleByteVectorCrossover(min, max, start, subpopulation, |
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115 | inds, state, thread); |
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116 | |
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117 | else if(inds[0] instanceof DoubleVectorIndividual) |
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118 | n = multipleDoubleVectorCrossover(min, max, start, subpopulation, |
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119 | inds, state, thread); |
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120 | |
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121 | else if(inds[0] instanceof FloatVectorIndividual) |
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122 | n = multipleFloatVectorCrossover(min, max, start, subpopulation, |
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123 | inds, state, thread); |
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124 | |
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125 | else if(inds[0] instanceof IntegerVectorIndividual) |
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126 | n = multipleIntegerVectorCrossover(min, max, start, subpopulation, |
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127 | inds, state, thread); |
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128 | |
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129 | else if(inds[0] instanceof GeneVectorIndividual) |
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130 | n = multipleGeneVectorCrossover(min, max, start, subpopulation, |
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131 | inds, state, thread); |
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132 | |
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133 | else if(inds[0] instanceof LongVectorIndividual) |
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134 | n = multipleLongVectorCrossover(min, max, start, subpopulation, |
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135 | inds, state, thread); |
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136 | |
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137 | else if(inds[0] instanceof ShortVectorIndividual) |
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138 | n = multipleShortVectorCrossover(min, max, start, subpopulation, |
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139 | inds, state, thread); |
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140 | |
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141 | else // default crossover -- shouldn't need this unless a new vector type is added |
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142 | { |
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143 | // check how many sources are provided |
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144 | if(sources.length <= 2) |
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145 | // this method shouldn't be called for just two parents |
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146 | state.output.error("Only two parents specified!"); |
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147 | |
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148 | |
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149 | // fill up parents: |
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150 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
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151 | { |
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152 | // produce one parent from each source |
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153 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
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154 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
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155 | parents[i] = (VectorIndividual)(parents[i].clone()); |
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156 | } |
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157 | |
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158 | |
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159 | //... some required intermediary steps .... |
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160 | |
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161 | // assuming all of the species are the same species ... |
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162 | VectorSpecies species = (VectorSpecies)parents[0].species; |
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163 | |
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164 | // an array of the split points (width = 1) |
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165 | int[] points = new int[(int)parents[0].genomeLength() - 1]; |
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166 | for(int i = 0; i < points.length; i++){ |
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167 | points[i] = i+1; // first split point/index = 1 |
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168 | } |
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169 | |
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170 | |
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171 | // split all the parents into object arrays |
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172 | Object[][] pieces = new Object[parents.length][(int)parents[0].genomeLength()]; |
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173 | |
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174 | // splitting... |
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175 | for(int i = 0; i < parents.length; i++){ |
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176 | if(parents[i].genomeLength() != parents[0].genomeLength()) |
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177 | state.output.fatal("All vectors must be of the same length for crossover!"); |
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178 | else |
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179 | parents[i].split(points, pieces[i]); |
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180 | } |
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181 | |
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182 | |
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183 | // crossing them over now |
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184 | for(int i = 0; i < pieces[0].length; i++) |
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185 | { |
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186 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
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187 | { |
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188 | // shuffle |
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189 | for(int j = pieces.length-1; j > 0; j--) // no need to shuffle first index at the end |
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190 | { |
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191 | // find parent to swap piece with |
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192 | int parent2 = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
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193 | // swap |
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194 | Object temp = pieces[j][i]; |
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195 | pieces[j][i] = pieces[parent2][i]; |
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196 | pieces[parent2][i] = temp; |
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197 | } |
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198 | } |
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199 | } |
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200 | |
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201 | // join them and add them to the population starting at the start location |
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202 | for(int i = 0, q = start; i < parents.length; i++, q++) |
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203 | { |
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204 | parents[i].join(pieces[i]); |
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205 | parents[i].evaluated = false; |
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206 | if(q<inds.length) // just in case |
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207 | { |
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208 | inds[q] = (VectorIndividual)parents[i]; |
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209 | } |
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210 | } |
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211 | } |
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212 | return n; |
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213 | } |
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214 | |
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215 | |
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216 | /** Crosses over the Bit Vector Individuals using a |
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217 | uniform crossover method. |
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218 | * |
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219 | * There is no need to call this method separately; produce(...) calls it |
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220 | * whenever necessary by default. |
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221 | */ |
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222 | public int multipleBitVectorCrossover(final int min, |
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223 | final int max, |
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224 | final int start, |
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225 | final int subpopulation, |
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226 | final Individual[] inds, |
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227 | final EvolutionState state, |
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228 | final int thread) |
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229 | { |
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230 | if(!(inds[0] instanceof BitVectorIndividual)) |
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231 | state.output.fatal("Trying to produce bit vector individuals when you can't!"); |
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232 | |
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233 | |
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234 | // check how many sources are provided |
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235 | if(sources.length <= 2) |
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236 | // this method shouldn't be called for just two parents |
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237 | state.output.error("Only two parents specified!"); |
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238 | |
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239 | |
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240 | // how many individuals should we make? |
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241 | int n = typicalIndsProduced(); |
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242 | if (n < min) n = min; |
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243 | if (n > max) n = max; |
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244 | |
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245 | |
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246 | |
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247 | // fill up parents: |
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248 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
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249 | { |
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250 | // produce one parent from each source |
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251 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
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252 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
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253 | parents[i] = (BitVectorIndividual)(parents[i].clone()); |
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254 | } |
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255 | |
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256 | |
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257 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
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258 | //this is dblvector or vector as long as we |
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259 | // can get the crossover probability |
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260 | |
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261 | |
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262 | // crossover |
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263 | for(int i = 0; i < parents[0].genomeLength(); i++) |
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264 | { |
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265 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
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266 | { |
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267 | for(int j = parents.length-1; j > 0; j--) |
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268 | { |
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269 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
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270 | boolean temp = ((BitVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
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271 | ((BitVectorIndividual)parents[j]).genome[i] = |
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272 | ((BitVectorIndividual)parents[swapIndex]).genome[i]; |
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273 | ((BitVectorIndividual)parents[swapIndex]).genome[i] = temp; |
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274 | } |
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275 | } |
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276 | } |
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277 | |
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278 | // add to population |
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279 | for(int i = 0, q = start; i < parents.length; i++, q++) |
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280 | { |
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281 | parents[i].evaluated = false; |
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282 | if(q<inds.length) // just in case |
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283 | { |
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284 | inds[q] = (BitVectorIndividual)parents[i]; |
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285 | } |
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286 | } |
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287 | return n; |
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288 | } |
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289 | |
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290 | |
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291 | /** Crosses over the Byte Vector Individuals using a |
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292 | uniform crossover method. |
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293 | * |
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294 | * There is no need to call this method separately; produce(...) calls it |
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295 | * whenever necessary by default. |
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296 | */ |
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297 | public int multipleByteVectorCrossover(final int min, |
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298 | final int max, |
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299 | final int start, |
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300 | final int subpopulation, |
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301 | final Individual[] inds, |
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302 | final EvolutionState state, |
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303 | final int thread) |
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304 | { |
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305 | if(!(inds[0] instanceof ByteVectorIndividual)) |
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306 | state.output.fatal("Trying to produce byte vector individuals when you can't!"); |
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307 | |
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308 | |
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309 | // check how many sources are provided |
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310 | if(sources.length <= 2) |
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311 | // this method shouldn't be called for just two parents |
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312 | state.output.error("Only two parents specified!"); |
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313 | |
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314 | |
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315 | // how many individuals should we make? |
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316 | int n = typicalIndsProduced(); |
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317 | if (n < min) n = min; |
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318 | if (n > max) n = max; |
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319 | |
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320 | |
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321 | |
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322 | // fill up parents: |
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323 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
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324 | { |
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325 | // produce one parent from each source |
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326 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
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327 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
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328 | parents[i] = (ByteVectorIndividual)(parents[i].clone()); |
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329 | } |
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330 | |
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331 | |
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332 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
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333 | //this is dblvector or vector as long as we |
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334 | // can get the crossover probability |
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335 | |
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336 | |
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337 | // crossover |
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338 | for(int i = 0; i < parents[0].genomeLength(); i++) |
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339 | { |
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340 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
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341 | { |
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342 | for(int j = parents.length-1; j > 0; j--) |
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343 | { |
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344 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
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345 | byte temp = ((ByteVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
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346 | ((ByteVectorIndividual)parents[j]).genome[i] = ((ByteVectorIndividual)parents[swapIndex]).genome[i]; |
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347 | ((ByteVectorIndividual)parents[swapIndex]).genome[i] = temp; |
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348 | } |
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349 | } |
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350 | } |
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351 | |
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352 | // add to population |
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353 | for(int i = 0, q = start; i < parents.length; i++, q++) |
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354 | { |
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355 | parents[i].evaluated = false; |
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356 | if(q<inds.length) // just in case |
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357 | { |
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358 | inds[q] = (ByteVectorIndividual)parents[i]; |
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359 | } |
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360 | } |
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361 | return n; |
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362 | } |
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363 | |
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364 | |
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365 | /** Crosses over the Double Vector Individuals using a |
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366 | uniform crossover method. |
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367 | * |
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368 | * There is no need to call this method separately; produce(...) calls it |
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369 | * whenever necessary by default. |
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370 | */ |
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371 | public int multipleDoubleVectorCrossover(final int min, |
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372 | final int max, |
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373 | final int start, |
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374 | final int subpopulation, |
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375 | final Individual[] inds, |
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376 | final EvolutionState state, |
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377 | final int thread) |
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378 | { |
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379 | |
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380 | if(!(inds[0] instanceof DoubleVectorIndividual)) |
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381 | state.output.fatal("Trying to produce double vector individuals when you can't!"); |
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382 | |
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383 | // check how many sources are provided |
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384 | if(sources.length <= 2) |
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385 | // this method shouldn't be called for just two parents |
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386 | state.output.error("Only two parents specified!"); |
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387 | |
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388 | |
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389 | // how many individuals should we make? |
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390 | int n = typicalIndsProduced(); |
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391 | if (n < min) n = min; |
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392 | if (n > max) n = max; |
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393 | |
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394 | |
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395 | |
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396 | // fill up parents: |
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397 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
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398 | { |
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399 | // produce one parent from each source |
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400 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
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401 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
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402 | parents[i] = (DoubleVectorIndividual)(parents[i].clone()); |
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403 | } |
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404 | |
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405 | |
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406 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
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407 | //this is dblvector or vector as long as we |
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408 | // can get the crossover probability |
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409 | |
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410 | |
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411 | // crossover |
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412 | for(int i = 0; i < parents[0].genomeLength(); i++) |
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413 | { |
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414 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
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415 | { |
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416 | for(int j = parents.length-1; j > 0; j--) |
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417 | { |
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418 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
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419 | double temp = ((DoubleVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
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420 | ((DoubleVectorIndividual)parents[j]).genome[i] = ((DoubleVectorIndividual)parents[swapIndex]).genome[i]; |
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421 | ((DoubleVectorIndividual)parents[swapIndex]).genome[i] = temp; |
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422 | } |
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423 | } |
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424 | } |
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425 | |
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426 | // add to population |
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427 | for(int i = 0, q = start; i < parents.length; i++, q++) |
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428 | { |
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429 | parents[i].evaluated = false; |
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430 | if(q<inds.length) // just in case |
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431 | { |
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432 | inds[q] = (DoubleVectorIndividual)parents[i]; |
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433 | } |
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434 | } |
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435 | return n; |
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436 | } |
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437 | |
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438 | |
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439 | /** Crosses over the Float Vector Individuals using a |
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440 | uniform crossover method. |
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441 | * |
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442 | * There is no need to call this method separately; produce(...) calls it |
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443 | * whenever necessary by default. |
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444 | */ |
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445 | public int multipleFloatVectorCrossover(final int min, |
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446 | final int max, |
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447 | final int start, |
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448 | final int subpopulation, |
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449 | final Individual[] inds, |
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450 | final EvolutionState state, |
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451 | final int thread) |
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452 | { |
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453 | if(!(inds[0] instanceof FloatVectorIndividual)) |
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454 | state.output.fatal("Trying to produce float vector individuals when you can't!"); |
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455 | |
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456 | |
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457 | // check how many sources are provided |
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458 | if(sources.length <= 2) |
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459 | // this method shouldn't be called for just two parents |
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460 | state.output.error("Only two parents specified!"); |
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461 | |
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462 | |
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463 | // how many individuals should we make? |
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464 | int n = typicalIndsProduced(); |
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465 | if (n < min) n = min; |
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466 | if (n > max) n = max; |
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467 | |
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468 | |
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469 | |
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470 | // fill up parents: |
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471 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
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472 | { |
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473 | // produce one parent from each source |
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474 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
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475 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
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476 | parents[i] = (FloatVectorIndividual)(parents[i].clone()); |
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477 | } |
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478 | |
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479 | |
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480 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
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481 | //this is dblvector or vector as long as we |
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482 | // can get the crossover probability |
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483 | |
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484 | |
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485 | // crossover |
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486 | for(int i = 0; i < parents[0].genomeLength(); i++) |
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487 | { |
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488 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
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489 | { |
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490 | for(int j = parents.length-1; j > 0; j--) |
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491 | { |
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492 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
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493 | float temp = ((FloatVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
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494 | ((FloatVectorIndividual)parents[j]).genome[i] = ((FloatVectorIndividual)parents[swapIndex]).genome[i]; |
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495 | ((FloatVectorIndividual)parents[swapIndex]).genome[i] = temp; |
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496 | } |
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497 | } |
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498 | } |
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499 | |
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500 | // add to population |
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501 | for(int i = 0, q = start; i < parents.length; i++, q++) |
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502 | { |
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503 | parents[i].evaluated = false; |
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504 | if(q<inds.length) // just in case |
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505 | { |
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506 | inds[q] = (FloatVectorIndividual)parents[i]; |
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507 | } |
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508 | } |
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509 | return n; |
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510 | } |
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511 | |
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512 | |
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513 | /** Crosses over the Gene Vector Individuals using a |
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514 | uniform crossover method. |
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515 | * |
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516 | * There is no need to call this method separately; produce(...) calls it |
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517 | * whenever necessary by default. |
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518 | */ |
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519 | public int multipleGeneVectorCrossover(final int min, |
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520 | final int max, |
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521 | final int start, |
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522 | final int subpopulation, |
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523 | final Individual[] inds, |
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524 | final EvolutionState state, |
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525 | final int thread) |
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526 | { |
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527 | if(!(inds[0] instanceof GeneVectorIndividual)) |
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528 | state.output.fatal("Trying to produce gene vector individuals when you can't!"); |
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529 | |
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530 | |
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531 | // check how many sources are provided |
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532 | if(sources.length <= 2) |
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533 | // this method shouldn't be called for just two parents |
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534 | state.output.error("Only two parents specified!"); |
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535 | |
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536 | |
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537 | // how many individuals should we make? |
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538 | int n = typicalIndsProduced(); |
---|
539 | if (n < min) n = min; |
---|
540 | if (n > max) n = max; |
---|
541 | |
---|
542 | |
---|
543 | |
---|
544 | // fill up parents: |
---|
545 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
---|
546 | { |
---|
547 | // produce one parent from each source |
---|
548 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
---|
549 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
---|
550 | parents[i] = (GeneVectorIndividual)(parents[i].clone()); |
---|
551 | } |
---|
552 | |
---|
553 | |
---|
554 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
---|
555 | //this is dblvector or vector as long as we |
---|
556 | // can get the crossover probability |
---|
557 | |
---|
558 | |
---|
559 | // crossover |
---|
560 | for(int i = 0; i < parents[0].genomeLength(); i++) |
---|
561 | { |
---|
562 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
---|
563 | { |
---|
564 | for(int j = parents.length-1; j > 0; j--) |
---|
565 | { |
---|
566 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
---|
567 | VectorGene temp = ((GeneVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
---|
568 | ((GeneVectorIndividual)parents[j]).genome[i] = ((GeneVectorIndividual)parents[swapIndex]).genome[i]; |
---|
569 | ((GeneVectorIndividual)parents[swapIndex]).genome[i] = temp; |
---|
570 | } |
---|
571 | } |
---|
572 | } |
---|
573 | |
---|
574 | // add to population |
---|
575 | for(int i = 0, q = start; i < parents.length; i++, q++) |
---|
576 | { |
---|
577 | parents[i].evaluated = false; |
---|
578 | if(q<inds.length) // just in case |
---|
579 | { |
---|
580 | inds[q] = (GeneVectorIndividual)parents[i]; |
---|
581 | } |
---|
582 | } |
---|
583 | return n; |
---|
584 | } |
---|
585 | |
---|
586 | |
---|
587 | /**Crosses over the Integer Vector Individuals using a uniform crossover method. |
---|
588 | * |
---|
589 | * There is no need to call this method separately; produce(...) calls it |
---|
590 | * whenever necessary by default. |
---|
591 | */ |
---|
592 | public int multipleIntegerVectorCrossover(final int min, |
---|
593 | final int max, |
---|
594 | final int start, |
---|
595 | final int subpopulation, |
---|
596 | final Individual[] inds, |
---|
597 | final EvolutionState state, |
---|
598 | final int thread) |
---|
599 | { |
---|
600 | |
---|
601 | if(!(inds[0] instanceof IntegerVectorIndividual)) |
---|
602 | state.output.fatal("Trying to produce integer vector individuals when you can't!"); |
---|
603 | |
---|
604 | |
---|
605 | |
---|
606 | // check how many sources are provided |
---|
607 | if(sources.length <= 2) |
---|
608 | // this method shouldn't be called for just two parents |
---|
609 | state.output.error("Only two parents specified!"); |
---|
610 | |
---|
611 | |
---|
612 | // how many individuals should we make? |
---|
613 | int n = typicalIndsProduced(); |
---|
614 | if (n < min) n = min; |
---|
615 | if (n > max) n = max; |
---|
616 | |
---|
617 | |
---|
618 | |
---|
619 | // fill up parents: |
---|
620 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
---|
621 | { |
---|
622 | // produce one parent from each source |
---|
623 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
---|
624 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
---|
625 | parents[i] = (IntegerVectorIndividual)(parents[i].clone()); |
---|
626 | |
---|
627 | } |
---|
628 | |
---|
629 | |
---|
630 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
---|
631 | //this is dblvector or vector as long as we |
---|
632 | // can get the crossover probability |
---|
633 | |
---|
634 | |
---|
635 | // crossover |
---|
636 | for(int i = 0; i < parents[0].genomeLength(); i++) |
---|
637 | { |
---|
638 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
---|
639 | { |
---|
640 | for(int j = parents.length-1; j > 0; j--) |
---|
641 | { |
---|
642 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
---|
643 | int temp = ((IntegerVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
---|
644 | ((IntegerVectorIndividual)parents[j]).genome[i] = ((IntegerVectorIndividual)parents[swapIndex]).genome[i]; |
---|
645 | ((IntegerVectorIndividual)parents[swapIndex]).genome[i] = temp; |
---|
646 | } |
---|
647 | } |
---|
648 | } |
---|
649 | |
---|
650 | // add to population |
---|
651 | for(int i = 0, q = start; i < parents.length; i++, q++) |
---|
652 | { |
---|
653 | parents[i].evaluated = false; |
---|
654 | if(q<inds.length) // just in case |
---|
655 | { |
---|
656 | inds[q] = (IntegerVectorIndividual)parents[i]; |
---|
657 | } |
---|
658 | } |
---|
659 | return n; |
---|
660 | } |
---|
661 | |
---|
662 | |
---|
663 | /** Crosses over the Long Vector Individuals using a |
---|
664 | uniform crossover method. |
---|
665 | * |
---|
666 | * There is no need to call this method separately; produce(...) calls it |
---|
667 | * whenever necessary by default. |
---|
668 | */ |
---|
669 | public int multipleLongVectorCrossover(final int min, |
---|
670 | final int max, |
---|
671 | final int start, |
---|
672 | final int subpopulation, |
---|
673 | final Individual[] inds, |
---|
674 | final EvolutionState state, |
---|
675 | final int thread) |
---|
676 | { |
---|
677 | if(!(inds[0] instanceof LongVectorIndividual)) |
---|
678 | state.output.fatal("Trying to produce long vector individuals when you can't!"); |
---|
679 | |
---|
680 | |
---|
681 | // check how many sources are provided |
---|
682 | if(sources.length <= 2) |
---|
683 | // this method shouldn't be called for just two parents |
---|
684 | state.output.error("Only two parents specified!"); |
---|
685 | |
---|
686 | |
---|
687 | // how many individuals should we make? |
---|
688 | int n = typicalIndsProduced(); |
---|
689 | if (n < min) n = min; |
---|
690 | if (n > max) n = max; |
---|
691 | |
---|
692 | |
---|
693 | |
---|
694 | // fill up parents: |
---|
695 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
---|
696 | { |
---|
697 | // produce one parent from each source |
---|
698 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
---|
699 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
---|
700 | parents[i] = (LongVectorIndividual)(parents[i].clone()); |
---|
701 | } |
---|
702 | |
---|
703 | |
---|
704 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
---|
705 | //this is dblvector or vector as long as we |
---|
706 | // can get the crossover probability |
---|
707 | |
---|
708 | // crossover |
---|
709 | for(int i = 0; i < parents[0].genomeLength(); i++) |
---|
710 | { |
---|
711 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
---|
712 | { |
---|
713 | for(int j = parents.length-1; j > 0; j--) |
---|
714 | { |
---|
715 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
---|
716 | long temp = ((LongVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
---|
717 | ((LongVectorIndividual)parents[j]).genome[i] = ((LongVectorIndividual)parents[swapIndex]).genome[i]; |
---|
718 | ((LongVectorIndividual)parents[swapIndex]).genome[i] = temp; |
---|
719 | } |
---|
720 | } |
---|
721 | } |
---|
722 | |
---|
723 | // add to population |
---|
724 | for(int i = 0, q = start; i < parents.length; i++, q++) |
---|
725 | { |
---|
726 | parents[i].evaluated = false; |
---|
727 | if(q<inds.length) // just in case |
---|
728 | { |
---|
729 | inds[q] = (LongVectorIndividual)parents[i]; |
---|
730 | } |
---|
731 | } |
---|
732 | return n; |
---|
733 | } |
---|
734 | |
---|
735 | |
---|
736 | /** Crosses over the Short Vector Individuals using a |
---|
737 | uniform crossover method. |
---|
738 | * |
---|
739 | * There is no need to call this method separately; produce(...) calls it |
---|
740 | * whenever necessary by default. |
---|
741 | */ |
---|
742 | public int multipleShortVectorCrossover(final int min, |
---|
743 | final int max, |
---|
744 | final int start, |
---|
745 | final int subpopulation, |
---|
746 | final Individual[] inds, |
---|
747 | final EvolutionState state, |
---|
748 | final int thread) |
---|
749 | { |
---|
750 | if(!(inds[0] instanceof ShortVectorIndividual)) |
---|
751 | state.output.fatal("Trying to produce short vector individuals when you can't!"); |
---|
752 | |
---|
753 | |
---|
754 | // check how many sources are provided |
---|
755 | if(sources.length <= 2) |
---|
756 | // this method shouldn't be called for just two parents |
---|
757 | state.output.error("Only two parents specified!"); |
---|
758 | |
---|
759 | |
---|
760 | // how many individuals should we make? |
---|
761 | int n = typicalIndsProduced(); |
---|
762 | if (n < min) n = min; |
---|
763 | if (n > max) n = max; |
---|
764 | |
---|
765 | |
---|
766 | |
---|
767 | // fill up parents: |
---|
768 | for(int i = 0;i<parents.length; i++) // parents.length == sources.length |
---|
769 | { |
---|
770 | // produce one parent from each source |
---|
771 | sources[i].produce(1,1,i,subpopulation,parents,state,thread); |
---|
772 | if (!(sources[i] instanceof BreedingPipeline)) // it's a selection method probably |
---|
773 | parents[i] = (ShortVectorIndividual)(parents[i].clone()); |
---|
774 | } |
---|
775 | |
---|
776 | |
---|
777 | VectorSpecies species = (VectorSpecies)inds[0].species; // doesn't really matter if |
---|
778 | //this is dblvector or vector as long as we |
---|
779 | // can get the crossover probability |
---|
780 | |
---|
781 | |
---|
782 | // crossover |
---|
783 | for(int i = 0; i < parents[0].genomeLength(); i++) |
---|
784 | { |
---|
785 | if(state.random[thread].nextBoolean(species.crossoverProbability)) |
---|
786 | { |
---|
787 | for(int j = parents.length-1; j > 0; j--) |
---|
788 | { |
---|
789 | int swapIndex = state.random[thread].nextInt(j); // not inclusive; don't want to swap with self |
---|
790 | short temp = ((ShortVectorIndividual) parents[j]).genome[i]; // modifying genomes directly. it's okay since they're clones |
---|
791 | ((ShortVectorIndividual)parents[j]).genome[i] = ((ShortVectorIndividual)parents[swapIndex]).genome[i]; |
---|
792 | ((ShortVectorIndividual)parents[swapIndex]).genome[i] = temp; |
---|
793 | } |
---|
794 | } |
---|
795 | } |
---|
796 | |
---|
797 | // add to population |
---|
798 | for(int i = 0, q = start; i < parents.length; i++, q++) |
---|
799 | { |
---|
800 | parents[i].evaluated = false; |
---|
801 | if(q<inds.length) // just in case |
---|
802 | { |
---|
803 | inds[q] = (ShortVectorIndividual)parents[i]; |
---|
804 | } |
---|
805 | } |
---|
806 | return n; |
---|
807 | } |
---|
808 | } |
---|