[6152] | 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 | |
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| 8 | package ec.vector; |
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| 9 | |
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| 10 | import ec.*; |
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| 11 | import ec.util.*; |
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| 12 | import java.io.*; |
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| 13 | |
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| 14 | /* |
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| 15 | * GeneVectorIndividual.java |
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| 16 | * Created: Thu Mar 22 13:13:20 EST 2001 |
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| 17 | */ |
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| 18 | |
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| 19 | /** |
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| 20 | * GeneVectorIndividual is a VectorIndividual whose genome is an array of VectorGenes. |
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| 21 | * The default mutation method calls the mutate() method on each gene independently |
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| 22 | * with <tt>species.mutationProbability</tt>. Initialization calls reset(), which |
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| 23 | * should call reset() on each gene. Do not expect that the genes will actually |
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| 24 | * exist during initialization -- see the default implementation of reset() as an example |
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| 25 | * for how to handle this. |
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| 26 | * |
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| 27 | |
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| 28 | * <P><b>From ec.Individual:</b> |
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| 29 | * |
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| 30 | * <p>In addition to serialization for checkpointing, Individuals may read and write themselves to streams in three ways. |
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| 31 | * |
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| 32 | * <ul> |
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| 33 | * <li><b>writeIndividual(...,DataOutput)/readIndividual(...,DataInput)</b> This method |
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| 34 | * transmits or receives an individual in binary. It is the most efficient approach to sending |
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| 35 | * individuals over networks, etc. These methods write the evaluated flag and the fitness, then |
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| 36 | * call <b>readGenotype/writeGenotype</b>, which you must implement to write those parts of your |
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| 37 | * Individual special to your functions-- the default versions of readGenotype/writeGenotype throw errors. |
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| 38 | * You don't need to implement them if you don't plan on using read/writeIndividual. |
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| 39 | * |
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| 40 | * <li><b>printIndividual(...,PrintWriter)/readIndividual(...,LineNumberReader)</b> This |
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| 41 | * approach transmits or receives an indivdual in text encoded such that the individual is largely readable |
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| 42 | * by humans but can be read back in 100% by ECJ as well. To do this, these methods will encode numbers |
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| 43 | * using the <tt>ec.util.Code</tt> class. These methods are mostly used to write out populations to |
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| 44 | * files for inspection, slight modification, then reading back in later on. <b>readIndividual</b> reads |
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| 45 | * in the fitness and the evaluation flag, then calls <b>parseGenotype</b> to read in the remaining individual. |
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| 46 | * You are responsible for implementing parseGenotype: the Code class is there to help you. |
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| 47 | * <b>printIndividual</b> writes out the fitness and evaluation flag, then calls <b>genotypeToString</b> |
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| 48 | * and printlns the resultant string. You are responsible for implementing the genotypeToString method in such |
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| 49 | * a way that parseGenotype can read back in the individual println'd with genotypeToString. The default form |
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| 50 | * of genotypeToString simply calls <b>toString</b>, which you may override instead if you like. The default |
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| 51 | * form of <b>parseGenotype</b> throws an error. You are not required to implement these methods, but without |
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| 52 | * them you will not be able to write individuals to files in a simultaneously computer- and human-readable fashion. |
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| 53 | * |
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| 54 | * <li><b>printIndividualForHumans(...,PrintWriter)</b> This |
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| 55 | * approach prints an individual in a fashion intended for human consumption only. |
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| 56 | * <b>printIndividualForHumans</b> writes out the fitness and evaluation flag, then calls <b>genotypeToStringForHumans</b> |
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| 57 | * and printlns the resultant string. You are responsible for implementing the genotypeToStringForHumans method. |
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| 58 | * The default form of genotypeToStringForHumans simply calls <b>toString</b>, which you may override instead if you like |
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| 59 | * (though note that genotypeToString's default also calls toString). You should handle one of these methods properly |
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| 60 | * to ensure individuals can be printed by ECJ. |
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| 61 | * </ul> |
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| 62 | |
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| 63 | * <p>In general, the various readers and writers do three things: they tell the Fitness to read/write itself, |
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| 64 | * they read/write the evaluated flag, and they read/write the gene array. If you add instance variables to |
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| 65 | * a VectorIndividual or subclass, you'll need to read/write those variables as well. |
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| 66 | <p><b>Default Base</b><br> |
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| 67 | vector.gene-vect-ind |
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| 68 | |
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| 69 | * @author Sean Luke |
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| 70 | * @version 1.0 |
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| 71 | */ |
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| 72 | |
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| 73 | public class GeneVectorIndividual extends VectorIndividual |
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| 74 | { |
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| 75 | public static final String P_GENEVECTORINDIVIDUAL = "gene-vect-ind"; |
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| 76 | public VectorGene[] genome; |
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| 77 | |
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| 78 | public Parameter defaultBase() |
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| 79 | { |
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| 80 | return VectorDefaults.base().push(P_GENEVECTORINDIVIDUAL); |
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| 81 | } |
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| 82 | |
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| 83 | public Object clone() |
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| 84 | { |
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| 85 | GeneVectorIndividual myobj = (GeneVectorIndividual) (super.clone()); |
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| 86 | |
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| 87 | // must clone the genome |
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| 88 | myobj.genome = (VectorGene[])(genome.clone()); |
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| 89 | for(int x=0;x<genome.length;x++) |
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| 90 | myobj.genome[x] = (VectorGene)(genome[x].clone()); |
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| 91 | |
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| 92 | return myobj; |
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| 93 | } |
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| 94 | |
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| 95 | public void setup(final EvolutionState state, final Parameter base) |
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| 96 | { |
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| 97 | super.setup(state,base); // actually unnecessary (Individual.setup() is empty) |
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| 98 | |
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| 99 | // since VectorSpecies set its constraint values BEFORE it called |
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| 100 | // super.setup(...) [which in turn called our setup(...)], we know that |
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| 101 | // stuff like genomeSize has already been set... |
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| 102 | |
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| 103 | Parameter def = defaultBase(); |
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| 104 | |
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| 105 | if (!(species instanceof GeneVectorSpecies)) |
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| 106 | state.output.fatal("GeneVectorIndividual requires a GeneVectorSpecies", base, def); |
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| 107 | GeneVectorSpecies s = (GeneVectorSpecies) species; |
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| 108 | |
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| 109 | // note that genome isn't initialized with any genes yet -- they're all null. |
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| 110 | // reset() needs |
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| 111 | genome = new VectorGene[s.genomeSize]; |
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| 112 | reset(state,0); |
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| 113 | } |
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| 114 | |
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| 115 | public void defaultCrossover(EvolutionState state, int thread, VectorIndividual ind) |
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| 116 | { |
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| 117 | GeneVectorSpecies s = (GeneVectorSpecies) species; |
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| 118 | GeneVectorIndividual i = (GeneVectorIndividual) ind; |
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| 119 | VectorGene tmp; |
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| 120 | int point; |
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| 121 | |
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| 122 | if (genome.length != i.genome.length) |
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| 123 | state.output.fatal("Genome lengths are not the same for fixed-length vector crossover"); |
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| 124 | switch(s.crossoverType) |
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| 125 | { |
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| 126 | case VectorSpecies.C_ONE_POINT: |
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| 127 | point = state.random[thread].nextInt((genome.length / s.chunksize)+1); |
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| 128 | for(int x=0;x<point*s.chunksize;x++) |
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| 129 | { |
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| 130 | tmp = i.genome[x]; |
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| 131 | i.genome[x] = genome[x]; |
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| 132 | genome[x] = tmp; |
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| 133 | } |
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| 134 | break; |
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| 135 | case VectorSpecies.C_TWO_POINT: |
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| 136 | int point0 = state.random[thread].nextInt((genome.length / s.chunksize)+1); |
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| 137 | point = state.random[thread].nextInt((genome.length / s.chunksize)+1); |
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| 138 | if (point0 > point) { int p = point0; point0 = point; point = p; } |
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| 139 | for(int x=point0*s.chunksize;x<point*s.chunksize;x++) |
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| 140 | { |
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| 141 | tmp = i.genome[x]; |
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| 142 | i.genome[x] = genome[x]; |
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| 143 | genome[x] = tmp; |
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| 144 | } |
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| 145 | break; |
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| 146 | case VectorSpecies.C_ANY_POINT: |
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| 147 | for(int x=0;x<genome.length/s.chunksize;x++) |
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| 148 | if (state.random[thread].nextBoolean(s.crossoverProbability)) |
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| 149 | for(int y=x*s.chunksize;y<(x+1)*s.chunksize;y++) |
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| 150 | { |
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| 151 | tmp = i.genome[y]; |
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| 152 | i.genome[y] = genome[y]; |
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| 153 | genome[y] = tmp; |
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| 154 | } |
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| 155 | break; |
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| 156 | } |
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| 157 | } |
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| 158 | |
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| 159 | /** Splits the genome into n pieces, according to points, which *must* be sorted. |
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| 160 | pieces.length must be 1 + points.length */ |
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| 161 | public void split(int[] points, Object[] pieces) |
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| 162 | { |
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| 163 | int point0, point1; |
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| 164 | point0 = 0; point1 = points[0]; |
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| 165 | for(int x=0;x<pieces.length;x++) |
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| 166 | { |
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| 167 | pieces[x] = new VectorGene[point1-point0]; |
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| 168 | System.arraycopy(genome,point0,pieces[x],0,point1-point0); |
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| 169 | point0 = point1; |
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| 170 | if (x >=pieces.length-2) |
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| 171 | point1 = genome.length; |
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| 172 | else point1 = points[x+1]; |
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| 173 | } |
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| 174 | } |
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| 175 | |
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| 176 | /** Joins the n pieces and sets the genome to their concatenation.*/ |
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| 177 | public void join(Object[] pieces) |
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| 178 | { |
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| 179 | int sum=0; |
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| 180 | for(int x=0;x<pieces.length;x++) |
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| 181 | sum += ((VectorGene[])(pieces[x])).length; |
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| 182 | |
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| 183 | int runningsum = 0; |
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| 184 | VectorGene[] newgenome = new VectorGene[sum]; |
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| 185 | for(int x=0;x<pieces.length;x++) |
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| 186 | { |
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| 187 | System.arraycopy(pieces[x], 0, newgenome, runningsum, ((VectorGene[])(pieces[x])).length); |
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| 188 | runningsum += ((VectorGene[])(pieces[x])).length; |
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| 189 | } |
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| 190 | // set genome |
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| 191 | genome = newgenome; |
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| 192 | } |
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| 193 | |
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| 194 | /** Destructively mutates the individual in some default manner. The default form |
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| 195 | simply randomizes genes to a uniform distribution from the min and max of the gene values. */ |
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| 196 | public void defaultMutate(EvolutionState state, int thread) |
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| 197 | { |
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| 198 | GeneVectorSpecies s = (GeneVectorSpecies) species; |
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| 199 | if (s.mutationProbability>0.0) |
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| 200 | for(int x=0;x<genome.length;x++) |
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| 201 | if (state.random[thread].nextBoolean(s.mutationProbability)) |
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| 202 | genome[x].mutate(state,thread); |
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| 203 | } |
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| 204 | |
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| 205 | /** Initializes the individual by calling reset(...) on each gene. */ |
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| 206 | public void reset(EvolutionState state, int thread) |
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| 207 | { |
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| 208 | GeneVectorSpecies s = (GeneVectorSpecies) species; |
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| 209 | |
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| 210 | for(int x=0;x<genome.length;x++) |
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| 211 | { |
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| 212 | // first create the gene if it doesn't exist |
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| 213 | if (genome[x]==null) genome[x] = (VectorGene)(s.genePrototype.clone()); |
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| 214 | // now reset it |
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| 215 | genome[x].reset(state,thread); |
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| 216 | } |
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| 217 | |
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| 218 | } |
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| 219 | |
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| 220 | public int hashCode() |
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| 221 | { |
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| 222 | // stolen from GPIndividual. It's a decent algorithm. |
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| 223 | int hash = this.getClass().hashCode(); |
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| 224 | |
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| 225 | for(int x=0;x<genome.length;x++) |
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| 226 | hash = ( hash << 1 | hash >>> 31 ) ^ genome[x].hashCode(); |
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| 227 | |
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| 228 | return hash; |
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| 229 | } |
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| 230 | |
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| 231 | public String genotypeToStringForHumans() |
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| 232 | { |
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| 233 | StringBuffer s = new StringBuffer(); |
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| 234 | for( int i = 0 ; i < genome.length ; i++ ) |
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| 235 | { s.append(" "); s.append(genome[i].printGeneToStringForHumans()); } |
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| 236 | return s.toString(); |
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| 237 | } |
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| 238 | |
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| 239 | public String genotypeToString() |
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| 240 | { |
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| 241 | StringBuffer s = new StringBuffer(); |
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| 242 | for( int i = 0 ; i < genome.length ; i++ ) |
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| 243 | { s.append(" "); s.append(genome[i].printGeneToString()); } |
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| 244 | return s.toString(); |
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| 245 | } |
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| 246 | |
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| 247 | protected void parseGenotype(final EvolutionState state, |
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| 248 | final LineNumberReader reader) throws IOException |
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| 249 | { |
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| 250 | // read in the next line. The first item is the number of genes |
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| 251 | String s = reader.readLine(); |
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| 252 | DecodeReturn d = new DecodeReturn(s); |
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| 253 | Code.decode( d ); |
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| 254 | int lll = (int)(d.l); |
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| 255 | |
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| 256 | genome = new VectorGene[ lll ]; |
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| 257 | |
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| 258 | GeneVectorSpecies _species = (GeneVectorSpecies) species; |
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| 259 | for( int i = 0 ; i < genome.length ; i++ ) |
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| 260 | { |
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| 261 | genome[i] = (VectorGene)(_species.genePrototype.clone()); |
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| 262 | genome[i].readGene(state,reader); |
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| 263 | } |
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| 264 | } |
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| 265 | |
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| 266 | public boolean equals(Object ind) |
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| 267 | { |
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| 268 | if (!(this.getClass().equals(ind.getClass()))) return false; |
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| 269 | GeneVectorIndividual i = (GeneVectorIndividual)ind; |
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| 270 | if( genome.length != i.genome.length ) |
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| 271 | return false; |
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| 272 | for( int j = 0 ; j < genome.length ; j++ ) |
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| 273 | if( !(genome[j].equals(i.genome[j]))) |
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| 274 | return false; |
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| 275 | return true; |
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| 276 | } |
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| 277 | |
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| 278 | public Object getGenome() |
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| 279 | { return genome; } |
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| 280 | public void setGenome(Object gen) |
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| 281 | { genome = (VectorGene[]) gen; } |
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| 282 | public int genomeLength() |
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| 283 | { return genome.length; } |
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| 284 | |
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| 285 | // clone all the genes |
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| 286 | public void cloneGenes(Object piece) |
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| 287 | { |
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| 288 | VectorGene[] genes = (VectorGene[]) piece; |
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| 289 | for(int i = 0 ; i < genes.length; i++) |
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| 290 | { |
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| 291 | if (genes[i] != null) genes[i] = (VectorGene)(genes[i].clone()); |
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| 292 | } |
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| 293 | } |
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| 294 | |
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| 295 | public void writeGenotype(final EvolutionState state, |
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| 296 | final DataOutput dataOutput) throws IOException |
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| 297 | { |
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| 298 | dataOutput.writeInt(genome.length); |
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| 299 | for(int x=0;x<genome.length;x++) |
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| 300 | genome[x].writeGene(state,dataOutput); |
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| 301 | } |
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| 302 | |
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| 303 | public void setGenomeLength(int len) |
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| 304 | { |
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| 305 | GeneVectorSpecies s = (GeneVectorSpecies) species; |
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| 306 | VectorGene[] newGenome = new VectorGene[len]; |
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| 307 | System.arraycopy(genome, 0, newGenome, 0, |
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| 308 | genome.length < newGenome.length ? genome.length : newGenome.length); |
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| 309 | for(int x=genome.length; x< newGenome.length; x++) |
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| 310 | if (genome[x]==null) genome[x] = (VectorGene)(s.genePrototype.clone()); // not reset |
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| 311 | genome = newGenome; |
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| 312 | } |
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| 313 | |
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| 314 | public void readGenotype(final EvolutionState state, |
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| 315 | final DataInput dataInput) throws IOException |
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| 316 | { |
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| 317 | int len = dataInput.readInt(); |
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| 318 | if (genome==null || genome.length != len) |
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| 319 | genome = new VectorGene[len]; |
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| 320 | GeneVectorSpecies _species = (GeneVectorSpecies) species; |
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| 321 | |
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| 322 | for(int x=0;x<genome.length;x++) |
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| 323 | { |
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| 324 | genome[x] = (VectorGene)(_species.genePrototype.clone()); |
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| 325 | genome[x].readGene(state,dataInput); |
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| 326 | } |
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| 327 | } |
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| 328 | |
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| 329 | } |
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