[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.gp.koza; |
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| 9 | import ec.util.*; |
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| 10 | import ec.*; |
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| 11 | import java.io.*; |
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| 12 | |
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| 13 | /* |
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| 14 | * KozaFitness.java |
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| 15 | * |
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| 16 | * Created: Fri Oct 15 14:26:44 1999 |
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| 17 | * By: Sean Luke |
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| 18 | */ |
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| 19 | |
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| 20 | /** |
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| 21 | * KozaFitness is a Fitness which stores an individual's fitness as described in |
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| 22 | * Koza I. Well, almost. In KozaFitness, standardized fitness and raw fitness |
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| 23 | * are considered the same (there are different methods for them, but they return |
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| 24 | * the same thing). Standardized fitness ranges from 0.0 inclusive (the best) |
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| 25 | * to infinity exclusive (the worst). Adjusted fitness converts this, using |
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| 26 | * the formula adj_f = 1/(1+f), into a scale from 0.0 exclusive (worst) to 1.0 |
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| 27 | * inclusive (best). While it's the standardized fitness that is stored, it |
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| 28 | * is the adjusted fitness that is printed out. |
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| 29 | * This is all just convenience stuff anyway; selection methods |
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| 30 | * generally don't use these fitness values but instead use the betterThan |
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| 31 | * and equalTo methods. |
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| 32 | * |
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| 33 | <p><b>Default Base</b><br> |
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| 34 | gp.koza.fitness |
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| 35 | * |
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| 36 | * |
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| 37 | * @author Sean Luke |
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| 38 | * @version 1.0 |
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| 39 | */ |
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| 40 | |
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| 41 | public class KozaFitness extends Fitness |
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| 42 | { |
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| 43 | public static final String P_KOZAFITNESS = "fitness"; |
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| 44 | |
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| 45 | /** This ranges from 0 (best) to infinity (worst). I |
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| 46 | define it here as equivalent to the standardized fitness. */ |
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| 47 | protected float standardizedFitness; |
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| 48 | |
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| 49 | /** This auxillary measure is used in some problems for additional |
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| 50 | information. It's a traditional feature of Koza-style GP, and so |
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| 51 | although I think it's not very useful, I'll leave it in anyway. */ |
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| 52 | public int hits; |
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| 53 | |
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| 54 | public Parameter defaultBase() |
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| 55 | { |
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| 56 | return GPKozaDefaults.base().push(P_KOZAFITNESS); |
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| 57 | } |
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| 58 | |
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| 59 | /** |
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| 60 | Do not use this function. Use the identical setStandardizedFitness() instead. |
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| 61 | The reason for the name change is that fitness() returns a differently-defined |
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| 62 | value than setFitness() sets, ugh. |
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| 63 | @deprecated |
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| 64 | */ |
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| 65 | public final void setFitness(final EvolutionState state, final float _f) |
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| 66 | { |
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| 67 | setStandardizedFitness(state,_f); |
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| 68 | } |
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| 69 | |
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| 70 | /** Set the standardized fitness in the half-open interval [0.0,infinity) |
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| 71 | which is defined (NOTE: DIFFERENT FROM fitness()!!!) as 0.0 |
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| 72 | being the IDEAL and infinity being worse than the worst possible. |
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| 73 | This is the GP tradition. The fitness() function instead will output |
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| 74 | the equivalent of Adjusted Fitness. |
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| 75 | */ |
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| 76 | public final void setStandardizedFitness(final EvolutionState state, final float _f) |
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| 77 | { |
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| 78 | if (_f < 0.0f || _f == Float.POSITIVE_INFINITY || Float.isNaN(_f)) |
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| 79 | { |
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| 80 | state.output.warning("Bad fitness (may not be < 0, NaN, or infinity): " + _f + ", setting to 0."); |
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| 81 | standardizedFitness = 0; |
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| 82 | } |
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| 83 | else standardizedFitness = _f; |
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| 84 | } |
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| 85 | |
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| 86 | /** Returns the adjusted fitness metric, which recasts the |
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| 87 | fitness to the half-open interval (0,1], where 1 is ideal and |
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| 88 | 0 is worst. Same as adjustedFitness(). */ |
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| 89 | |
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| 90 | public final float fitness() |
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| 91 | { |
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| 92 | return 1.0f/(1.0f+standardizedFitness); |
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| 93 | } |
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| 94 | |
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| 95 | /** Returns the raw fitness metric. |
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| 96 | @deprecated use standardizedFitness() |
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| 97 | */ |
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| 98 | public final float rawFitness() |
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| 99 | { |
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| 100 | return standardizedFitness(); |
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| 101 | } |
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| 102 | |
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| 103 | /** Returns the standardized fitness metric. */ |
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| 104 | |
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| 105 | public final float standardizedFitness() |
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| 106 | { |
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| 107 | return standardizedFitness; |
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| 108 | } |
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| 109 | |
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| 110 | /** Returns the adjusted fitness metric, which recasts the fitness |
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| 111 | to the half-open interval (0,1], where 1 is ideal and 0 is worst. |
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| 112 | This metric is used when printing the fitness out. */ |
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| 113 | |
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| 114 | public final float adjustedFitness() |
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| 115 | { |
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| 116 | return fitness(); |
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| 117 | } |
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| 118 | |
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| 119 | public void setup(final EvolutionState state, final Parameter base) { } |
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| 120 | |
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| 121 | public final boolean isIdealFitness() |
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| 122 | { |
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| 123 | return standardizedFitness == 0.0f; |
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| 124 | } |
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| 125 | |
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| 126 | public boolean equivalentTo(final Fitness _fitness) |
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| 127 | { |
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| 128 | return _fitness.fitness() == fitness(); |
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| 129 | } |
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| 130 | |
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| 131 | public boolean betterThan(final Fitness _fitness) |
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| 132 | { |
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| 133 | return _fitness.fitness() < fitness(); |
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| 134 | } |
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| 135 | |
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| 136 | public String fitnessToString() |
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| 137 | { |
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| 138 | return FITNESS_PREAMBLE + Code.encode(standardizedFitness) + Code.encode(hits); |
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| 139 | } |
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| 140 | |
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| 141 | public String fitnessToStringForHumans() |
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| 142 | { |
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| 143 | return FITNESS_PREAMBLE + "Standardized=" + standardizedFitness + " Adjusted=" + adjustedFitness() + " Hits=" + hits; |
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| 144 | } |
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| 145 | |
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| 146 | public final void readFitness(final EvolutionState state, |
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| 147 | final LineNumberReader reader) |
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| 148 | throws IOException |
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| 149 | { |
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| 150 | DecodeReturn d = Code.checkPreamble(FITNESS_PREAMBLE, state, reader); |
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| 151 | |
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| 152 | // extract fitness |
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| 153 | Code.decode(d); |
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| 154 | if (d.type!=DecodeReturn.T_FLOAT) |
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| 155 | state.output.fatal("Reading Line " + d.lineNumber + ": " + |
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| 156 | "Bad Fitness."); |
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| 157 | standardizedFitness = (float)d.d; |
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| 158 | |
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| 159 | // extract hits |
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| 160 | Code.decode(d); |
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| 161 | if (d.type!=DecodeReturn.T_INT) |
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| 162 | state.output.fatal("Reading Line " + d.lineNumber + ": " + |
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| 163 | "Bad Fitness."); |
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| 164 | hits = (int)d.l; |
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| 165 | } |
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| 166 | |
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| 167 | public void writeFitness(final EvolutionState state, |
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| 168 | final DataOutput dataOutput) throws IOException |
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| 169 | { |
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| 170 | dataOutput.writeFloat(standardizedFitness); |
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| 171 | dataOutput.writeInt(hits); |
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| 172 | } |
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| 173 | |
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| 174 | public void readFitness(final EvolutionState state, |
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| 175 | final DataInput dataInput) throws IOException |
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| 176 | { |
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| 177 | standardizedFitness = dataInput.readFloat(); |
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| 178 | hits = dataInput.readInt(); |
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| 179 | } |
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| 180 | |
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| 181 | public void setToMeanOf(EvolutionState state, Fitness[] fitnesses) |
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| 182 | { |
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| 183 | // this is not numerically stable. Perhaps we should have a numerically stable algorithm for sums |
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| 184 | // we're presuming it's not a very large number of elements, so it's probably not a big deal, |
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| 185 | // since this function is meant to be used mostly for gathering trials together. |
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| 186 | double f = 0; |
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| 187 | long h = 0; |
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| 188 | for(int i = 0; i < fitnesses.length; i++) |
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| 189 | { |
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| 190 | KozaFitness fit = (KozaFitness)(fitnesses[i]); |
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| 191 | f += fit.standardizedFitness; |
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| 192 | h += fit.hits; |
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| 193 | } |
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| 194 | f /= fitnesses.length; |
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| 195 | h /= fitnesses.length; |
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| 196 | standardizedFitness = (float)f; |
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| 197 | hits = (int)h; |
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| 198 | } |
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| 199 | } |
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