[645] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17180] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[645] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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[4068] | 22 | using System;
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[645] | 23 | using System.Collections.Generic;
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[4068] | 24 | using System.Linq;
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[4722] | 25 | using HeuristicLab.Common;
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[645] | 26 | using HeuristicLab.Core;
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[3237] | 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Parameters;
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[16565] | 29 | using HEAL.Attic;
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[12422] | 30 | using HeuristicLab.Random;
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[645] | 31 |
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[5499] | 32 | namespace HeuristicLab.Encodings.SymbolicExpressionTreeEncoding {
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[3237] | 33 | /// <summary>
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| 34 | /// Takes two parent individuals P0 and P1 each. Selects a random node N0 of P0 and a random node N1 of P1.
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| 35 | /// And replaces the branch with root0 N0 in P0 with N1 from P1 if the tree-size limits are not violated.
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| 36 | /// When recombination with N0 and N1 would create a tree that is too large or invalid the operator randomly selects new N0 and N1
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| 37 | /// until a valid configuration is found.
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| 38 | /// </summary>
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[7506] | 39 | [Item("SubtreeSwappingCrossover", "An operator which performs subtree swapping crossover.")]
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[16565] | 40 | [StorableType("2A2552C0-11C8-4F60-90B2-5FDDD3AB2444")]
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[7506] | 41 | public class SubtreeCrossover : SymbolicExpressionTreeCrossover, ISymbolicExpressionTreeSizeConstraintOperator {
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[5499] | 42 | private const string InternalCrossoverPointProbabilityParameterName = "InternalCrossoverPointProbability";
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| 43 | private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
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| 44 | private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
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[17490] | 45 | private const string CrossoverProbabilityParameterName = "CrossoverProbability";
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[7506] | 46 |
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[5499] | 47 | #region Parameter Properties
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[3237] | 48 | public IValueLookupParameter<PercentValue> InternalCrossoverPointProbabilityParameter {
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[5499] | 49 | get { return (IValueLookupParameter<PercentValue>)Parameters[InternalCrossoverPointProbabilityParameterName]; }
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[645] | 50 | }
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[5499] | 51 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
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| 52 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
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| 53 | }
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| 54 | public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeDepthParameter {
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| 55 | get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeDepthParameterName]; }
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| 56 | }
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[17490] | 57 | public IFixedValueParameter<DoubleValue> CrossoverProbabilityParameter {
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| 58 | get { return (IFixedValueParameter<DoubleValue>)Parameters[CrossoverProbabilityParameterName]; }
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| 59 | }
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[5499] | 60 | #endregion
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| 61 | #region Properties
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| 62 | public PercentValue InternalCrossoverPointProbability {
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| 63 | get { return InternalCrossoverPointProbabilityParameter.ActualValue; }
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| 64 | }
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| 65 | public IntValue MaximumSymbolicExpressionTreeLength {
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| 66 | get { return MaximumSymbolicExpressionTreeLengthParameter.ActualValue; }
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| 67 | }
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| 68 | public IntValue MaximumSymbolicExpressionTreeDepth {
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| 69 | get { return MaximumSymbolicExpressionTreeDepthParameter.ActualValue; }
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| 70 | }
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[17490] | 71 | public double CrossoverProbability {
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| 72 | get { return CrossoverProbabilityParameter.Value.Value; }
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| 73 | set { CrossoverProbabilityParameter.Value.Value = value; }
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| 74 | }
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[5499] | 75 | #endregion
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[4722] | 76 | [StorableConstructor]
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[16565] | 77 | protected SubtreeCrossover(StorableConstructorFlag _) : base(_) { }
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[7506] | 78 | protected SubtreeCrossover(SubtreeCrossover original, Cloner cloner) : base(original, cloner) { }
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[3237] | 79 | public SubtreeCrossover()
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| 80 | : base() {
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[5499] | 81 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, "The maximal length (number of nodes) of the symbolic expression tree."));
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| 82 | Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeDepthParameterName, "The maximal depth of the symbolic expression tree (a tree with one node has depth = 0)."));
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| 83 | Parameters.Add(new ValueLookupParameter<PercentValue>(InternalCrossoverPointProbabilityParameterName, "The probability to select an internal crossover point (instead of a leaf node).", new PercentValue(0.9)));
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[17490] | 84 | Parameters.Add(new FixedValueParameter<DoubleValue>(CrossoverProbabilityParameterName, "", new DoubleValue(1)));
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[3237] | 85 | }
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| 86 |
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[17490] | 87 | [StorableHook(HookType.AfterDeserialization)]
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| 88 | private void AfterDeserialization() {
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| 89 | if (!Parameters.ContainsKey(CrossoverProbabilityParameterName)) {
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| 90 | Parameters.Add(new FixedValueParameter<DoubleValue>(CrossoverProbabilityParameterName, "", new DoubleValue(1)));
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| 91 | }
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| 92 | }
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| 93 |
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[4722] | 94 | public override IDeepCloneable Clone(Cloner cloner) {
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| 95 | return new SubtreeCrossover(this, cloner);
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| 96 | }
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| 97 |
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[7506] | 98 | public override ISymbolicExpressionTree Crossover(IRandom random,
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[5510] | 99 | ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
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[17490] | 100 | return Cross(random, parent0, parent1, CrossoverProbability, InternalCrossoverPointProbability.Value,
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[5499] | 101 | MaximumSymbolicExpressionTreeLength.Value, MaximumSymbolicExpressionTreeDepth.Value);
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[3237] | 102 | }
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| 103 |
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[5510] | 104 | public static ISymbolicExpressionTree Cross(IRandom random,
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| 105 | ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
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[17490] | 106 | double probability,
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[5549] | 107 | double internalCrossoverPointProbability, int maxTreeLength, int maxTreeDepth) {
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[17491] | 108 | if ((probability < 1) && (random.NextDouble() >= probability)) return random.NextDouble() < 0.5 ? parent0 : parent1;
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[3294] | 109 | // select a random crossover point in the first parent
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[5916] | 110 | CutPoint crossoverPoint0;
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| 111 | SelectCrossoverPoint(random, parent0, internalCrossoverPointProbability, maxTreeLength, maxTreeDepth, out crossoverPoint0);
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[645] | 112 |
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[5916] | 113 | int childLength = crossoverPoint0.Child != null ? crossoverPoint0.Child.GetLength() : 0;
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[5549] | 114 | // calculate the max length and depth that the inserted branch can have
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[14198] | 115 | int maxInsertedBranchLength = Math.Max(0, maxTreeLength - (parent0.Length - childLength));
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| 116 | int maxInsertedBranchDepth = Math.Max(0, maxTreeDepth - parent0.Root.GetBranchLevel(crossoverPoint0.Parent));
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[645] | 117 |
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[5510] | 118 | List<ISymbolicExpressionTreeNode> allowedBranches = new List<ISymbolicExpressionTreeNode>();
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[3997] | 119 | parent1.Root.ForEachNodePostfix((n) => {
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[5549] | 120 | if (n.GetLength() <= maxInsertedBranchLength &&
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[7506] | 121 | n.GetDepth() <= maxInsertedBranchDepth && crossoverPoint0.IsMatchingPointType(n))
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[3997] | 122 | allowedBranches.Add(n);
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| 123 | });
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[5916] | 124 | // empty branch
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[7506] | 125 | if (crossoverPoint0.IsMatchingPointType(null)) allowedBranches.Add(null);
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[645] | 126 |
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[3997] | 127 | if (allowedBranches.Count == 0) {
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[3297] | 128 | return parent0;
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| 129 | } else {
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[3294] | 130 | var selectedBranch = SelectRandomBranch(random, allowedBranches, internalCrossoverPointProbability);
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[14221] | 131 | if (selectedBranch != null)
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| 132 | selectedBranch = (ISymbolicExpressionTreeNode)selectedBranch.Clone();
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[645] | 133 |
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[5916] | 134 | if (crossoverPoint0.Child != null) {
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| 135 | // manipulate the tree of parent0 in place
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| 136 | // replace the branch in tree0 with the selected branch from tree1
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| 137 | crossoverPoint0.Parent.RemoveSubtree(crossoverPoint0.ChildIndex);
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| 138 | if (selectedBranch != null) {
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| 139 | crossoverPoint0.Parent.InsertSubtree(crossoverPoint0.ChildIndex, selectedBranch);
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| 140 | }
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| 141 | } else {
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| 142 | // child is null (additional child should be added under the parent)
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| 143 | if (selectedBranch != null) {
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| 144 | crossoverPoint0.Parent.AddSubtree(selectedBranch);
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| 145 | }
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| 146 | }
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[3294] | 147 | return parent0;
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[645] | 148 | }
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| 149 | }
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| 150 |
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[5916] | 151 | private static void SelectCrossoverPoint(IRandom random, ISymbolicExpressionTree parent0, double internalNodeProbability, int maxBranchLength, int maxBranchDepth, out CutPoint crossoverPoint) {
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[3997] | 152 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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[5686] | 153 | List<CutPoint> internalCrossoverPoints = new List<CutPoint>();
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| 154 | List<CutPoint> leafCrossoverPoints = new List<CutPoint>();
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[3997] | 155 | parent0.Root.ForEachNodePostfix((n) => {
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[7506] | 156 | if (n.SubtreeCount > 0 && n != parent0.Root) {
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[12509] | 157 | //avoid linq to reduce memory pressure
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| 158 | for (int i = 0; i < n.SubtreeCount; i++) {
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| 159 | var child = n.GetSubtree(i);
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[5549] | 160 | if (child.GetLength() <= maxBranchLength &&
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| 161 | child.GetDepth() <= maxBranchDepth) {
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[7506] | 162 | if (child.SubtreeCount > 0)
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[5686] | 163 | internalCrossoverPoints.Add(new CutPoint(n, child));
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[5367] | 164 | else
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[5686] | 165 | leafCrossoverPoints.Add(new CutPoint(n, child));
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[5367] | 166 | }
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[3997] | 167 | }
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[7506] | 168 |
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[5916] | 169 | // add one additional extension point if the number of sub trees for the symbol is not full
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[6803] | 170 | if (n.SubtreeCount < n.Grammar.GetMaximumSubtreeCount(n.Symbol)) {
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[5916] | 171 | // empty extension point
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[6803] | 172 | internalCrossoverPoints.Add(new CutPoint(n, n.SubtreeCount));
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[5916] | 173 | }
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[3997] | 174 | }
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[7506] | 175 | }
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| 176 | );
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[5367] | 177 |
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[3997] | 178 | if (random.NextDouble() < internalNodeProbability) {
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| 179 | // select from internal node if possible
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| 180 | if (internalCrossoverPoints.Count > 0) {
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| 181 | // select internal crossover point or leaf
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[5916] | 182 | crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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[3997] | 183 | } else {
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| 184 | // otherwise select external node
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[5916] | 185 | crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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[3997] | 186 | }
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| 187 | } else if (leafCrossoverPoints.Count > 0) {
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| 188 | // select from leaf crossover point if possible
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[5916] | 189 | crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
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[3997] | 190 | } else {
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| 191 | // otherwise select internal crossover point
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[5916] | 192 | crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
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[645] | 193 | }
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| 194 | }
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[3237] | 195 |
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[5510] | 196 | private static ISymbolicExpressionTreeNode SelectRandomBranch(IRandom random, IEnumerable<ISymbolicExpressionTreeNode> branches, double internalNodeProbability) {
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[3237] | 197 | if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
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[5510] | 198 | List<ISymbolicExpressionTreeNode> allowedInternalBranches;
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| 199 | List<ISymbolicExpressionTreeNode> allowedLeafBranches;
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[3997] | 200 | if (random.NextDouble() < internalNodeProbability) {
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| 201 | // select internal node if possible
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| 202 | allowedInternalBranches = (from branch in branches
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[7506] | 203 | where branch != null && branch.SubtreeCount > 0
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[3997] | 204 | select branch).ToList();
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| 205 | if (allowedInternalBranches.Count > 0) {
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[12422] | 206 | return allowedInternalBranches.SampleRandom(random);
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| 207 |
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[3997] | 208 | } else {
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| 209 | // no internal nodes allowed => select leaf nodes
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| 210 | allowedLeafBranches = (from branch in branches
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[7506] | 211 | where branch == null || branch.SubtreeCount == 0
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[3989] | 212 | select branch).ToList();
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[12422] | 213 | return allowedLeafBranches.SampleRandom(random);
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[3997] | 214 | }
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[3237] | 215 | } else {
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[3997] | 216 | // select leaf node if possible
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| 217 | allowedLeafBranches = (from branch in branches
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[7506] | 218 | where branch == null || branch.SubtreeCount == 0
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[3997] | 219 | select branch).ToList();
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| 220 | if (allowedLeafBranches.Count > 0) {
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[12422] | 221 | return allowedLeafBranches.SampleRandom(random);
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[3997] | 222 | } else {
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| 223 | allowedInternalBranches = (from branch in branches
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[7506] | 224 | where branch != null && branch.SubtreeCount > 0
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[3997] | 225 | select branch).ToList();
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[12422] | 226 | return allowedInternalBranches.SampleRandom(random);
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| 227 |
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[3997] | 228 | }
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[3237] | 229 | }
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| 230 | }
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[645] | 231 | }
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| 232 | }
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