[15275] | 1 | namespace HeuristicLab.Problems.ProgramSynthesis.Push.Crossover {
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| 2 | using System;
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| 3 | using System.Linq;
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| 4 |
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| 5 | using HeuristicLab.Common;
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| 6 | using HeuristicLab.Core;
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| 7 | using HeuristicLab.Data;
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| 8 | using HeuristicLab.Operators;
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| 9 | using HeuristicLab.Optimization;
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| 10 | using HeuristicLab.Parameters;
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| 11 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[15289] | 12 | using HeuristicLab.Problems.ProgramSynthesis.Base.Extensions;
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[15275] | 13 | using HeuristicLab.Problems.ProgramSynthesis.Push.Encoding;
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| 14 | using HeuristicLab.Random;
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| 15 |
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| 16 | /// <summary>
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| 17 | /// Alternation crossover for plush vectors.
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| 18 | /// </summary>
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| 19 | [Item("AlternationCrossover", "Alternation crossover for plush vectors.")]
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| 20 | [StorableClass]
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| 21 | public class AlternationCrossover : InstrumentedOperator, IPlushCrossover, IStochasticOperator {
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| 22 | private const double Mean = 0.0;
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| 23 |
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| 24 | public AlternationCrossover() {
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| 25 | Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used for stochastic crossover operators."));
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[15289] | 26 |
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[15275] | 27 | Parameters.Add(new ScopeTreeLookupParameter<PlushVector>("Parents", "The parent vectors which should be crossed."));
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| 28 | ParentsParameter.ActualName = "PlushVector";
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[15289] | 29 |
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[15275] | 30 | Parameters.Add(new LookupParameter<PlushVector>("Child", "The child vector resulting from the crossover."));
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| 31 | ChildParameter.ActualName = "PlushVector";
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[15289] | 32 |
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[15334] | 33 | Parameters.Add(new LookupParameter<IntValue>("MaxProgramLength", "The max length of children"));
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| 34 |
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[15275] | 35 | Parameters.Add(new FixedValueParameter<PercentValue>("AlternationRate", "Specifies the probability of switching to another parent.", new PercentValue(0.5)));
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| 36 | Parameters.Add(new FixedValueParameter<DoubleValue>("AlignmentDeviation", "When alternating between parents, the index at which to continue copying may be offset backward or forward some amount based on a random sample from a normal distribution with mean 0 and standard deviation set by the alignment deviation parameter", new DoubleValue(1.0)));
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[15334] | 37 |
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[15275] | 38 | }
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| 39 |
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| 40 | [StorableConstructor]
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| 41 | public AlternationCrossover(bool deserializing) : base(deserializing) {
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| 42 | }
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| 43 |
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| 44 | public AlternationCrossover(AlternationCrossover origin, Cloner cloner) : base(origin, cloner) {
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| 45 | }
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| 46 |
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| 47 | public override IDeepCloneable Clone(Cloner cloner) {
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| 48 | return new AlternationCrossover(this, cloner);
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| 49 | }
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| 50 |
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[15334] | 51 | public ILookupParameter<IntValue> MaxProgramLengthParameter
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[15275] | 52 | {
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[15334] | 53 | get { return (ILookupParameter<IntValue>)Parameters["MaxProgramLength"]; }
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[15275] | 54 | }
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| 55 |
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[15334] | 56 | public int MaxProgramLength
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[15275] | 57 | {
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[15334] | 58 | get { return MaxProgramLengthParameter.ActualValue.Value; }
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| 59 | set { MaxProgramLengthParameter.ActualValue.Value = value; }
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[15275] | 60 | }
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| 61 |
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| 62 | public IValueParameter<PercentValue> AlternationRateParameter
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| 63 | {
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| 64 | get { return (IValueParameter<PercentValue>)Parameters["AlternationRate"]; }
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| 65 | }
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| 66 |
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| 67 | public double AlternationRate
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| 68 | {
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| 69 | get { return AlternationRateParameter.Value.Value; }
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| 70 | set { AlternationRateParameter.Value.Value = value; }
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| 71 | }
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| 72 |
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| 73 | public IValueParameter<DoubleValue> AlignmentDeviationParameter
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| 74 | {
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| 75 | get { return (IValueParameter<DoubleValue>)Parameters["AlignmentDeviation"]; }
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| 76 | }
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| 77 |
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| 78 | public double AlignmentDeviation
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| 79 | {
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| 80 | get { return AlignmentDeviationParameter.Value.Value; }
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| 81 | set { AlignmentDeviationParameter.Value.Value = value; }
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| 82 | }
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| 83 |
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| 84 | public ILookupParameter<IRandom> RandomParameter
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| 85 | {
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| 86 | get { return (LookupParameter<IRandom>)Parameters["Random"]; }
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| 87 | }
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| 88 | public ILookupParameter<ItemArray<PlushVector>> ParentsParameter
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| 89 | {
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| 90 | get { return (ScopeTreeLookupParameter<PlushVector>)Parameters["Parents"]; }
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| 91 | }
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| 92 | public ILookupParameter<PlushVector> ChildParameter
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| 93 | {
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| 94 | get { return (ILookupParameter<PlushVector>)Parameters["Child"]; }
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| 95 | }
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| 96 |
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| 97 | public sealed override IOperation InstrumentedApply() {
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[15289] | 98 | ChildParameter.ActualValue = Cross(
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| 99 | RandomParameter.ActualValue,
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| 100 | ParentsParameter.ActualValue,
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| 101 | AlternationRate,
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[15334] | 102 | MaxProgramLength,
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[15289] | 103 | AlignmentDeviation);
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[15275] | 104 | return base.InstrumentedApply();
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| 105 | }
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| 106 |
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[15289] | 107 | private static PlushVector Cross(
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| 108 | IRandom random,
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| 109 | ItemArray<PlushVector> parents,
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| 110 | double alternationRate,
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| 111 | int maxChildLength,
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| 112 | double alignmentDeviation) {
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| 113 | var normalDistributedRandom = new NormalDistributedRandom(random, Mean, alignmentDeviation);
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[15275] | 114 | var maxLength = parents.Max(p => p.Entries.Count);
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| 115 | var parentIndex = random.Next(0, 2);
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| 116 | var parent = parents[parentIndex];
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| 117 | var child = new PlushVector(maxLength);
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| 118 |
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| 119 | for (var i = 0; i < maxLength && child.Entries.Count <= maxChildLength; i++) {
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| 120 |
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| 121 | // if parent is shorter than the other, then ignore those entries
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| 122 | if (i < parent.Entries.Count)
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| 123 | child.Add(parent[i]);
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| 124 |
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| 125 | // switch parent?
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[15289] | 126 | if (random.NextDouble() < alternationRate) {
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[15275] | 127 | parentIndex = parentIndex == 0 ? 1 : 0;
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| 128 | parent = parents[parentIndex];
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[15289] | 129 | i += normalDistributedRandom.NextRounded();
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[15275] | 130 | i = Math.Max(i, 0);
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| 131 | }
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| 132 | }
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| 133 |
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| 134 | return child;
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| 135 | }
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| 136 | }
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| 137 | }
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