[7476] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[7476] | 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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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[7481] | 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[12422] | 29 | using HeuristicLab.Random;
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[7476] | 30 |
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| 31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[7494] | 32 | [Item("ProbabilisticFunctionalCrossover", "An operator which performs subtree swapping based on the behavioral similarity between subtrees:\n" +
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| 33 | "- Take two parent individuals P0 and P1\n" +
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| 34 | "- Randomly choose a node N from P0\n" +
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| 35 | "- For each matching node M from P1, calculate the behavioral distance:\n" +
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| 36 | "\t\tD(N,M) = 0.5 * ( abs(max(N) - max(M)) + abs(min(N) - min(M)) )\n" +
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| 37 | "- Make a probabilistic weighted choice of node M from P1, based on the inversed and normalized behavioral distance")]
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[13395] | 38 | [StorableClass]
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[7476] | 39 | public sealed class SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
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| 40 | [StorableConstructor]
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| 41 | private SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(bool deserializing) : base(deserializing) { }
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| 42 | private SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
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| 43 | : base(original, cloner) { }
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| 44 | public SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover()
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| 45 | : base() {
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[7521] | 46 | name = "ProbabilisticFunctionalCrossover";
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[7476] | 47 | }
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| 48 | public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover<T>(this, cloner); }
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| 49 |
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[7481] | 50 | public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
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[7476] | 51 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
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| 52 | List<int> rows = GenerateRowsToEvaluate().ToList();
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| 53 | T problemData = ProblemDataParameter.ActualValue;
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| 54 | return Cross(random, parent0, parent1, interpreter, problemData,
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| 55 | rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value);
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| 56 | }
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| 57 |
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| 58 | /// <summary>
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| 59 | /// Takes two parent individuals P0 and P1.
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| 60 | /// Randomly choose a node i from the first parent, then for each matching node j from the second parent, calculate the behavioral distance based on the range:
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| 61 | /// d(i,j) = 0.5 * ( abs(max(i) - max(j)) + abs(min(i) - min(j)) ).
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[7494] | 62 | /// Next, assign probabilities for the selection of a node j based on the inversed and normalized behavioral distance, then make a random weighted choice.
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[7476] | 63 | /// </summary>
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| 64 | public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
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| 65 | ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, T problemData, IList<int> rows, int maxDepth, int maxLength) {
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| 66 | var crossoverPoints0 = new List<CutPoint>();
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| 67 | parent0.Root.ForEachNodePostfix((n) => {
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[7494] | 68 | // the if clauses prevent the root or the startnode from being selected, although the startnode can be the parent of the node being swapped.
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| 69 | if (n.Parent != null && n.Parent != parent0.Root) {
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| 70 | crossoverPoints0.Add(new CutPoint(n.Parent, n));
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| 71 | }
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[7476] | 72 | });
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[12422] | 73 |
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| 74 | var crossoverPoint0 = crossoverPoints0.SampleRandom(random);
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[7476] | 75 | int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
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| 76 | int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
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| 77 |
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| 78 | var allowedBranches = new List<ISymbolicExpressionTreeNode>();
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| 79 | parent1.Root.ForEachNodePostfix((n) => {
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[7494] | 80 | if (n.Parent != null && n.Parent != parent1.Root) {
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| 81 | if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
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| 82 | allowedBranches.Add(n);
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| 83 | }
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[7476] | 84 | });
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| 85 |
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| 86 | if (allowedBranches.Count == 0)
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| 87 | return parent0;
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| 88 |
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| 89 | var dataset = problemData.Dataset;
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| 90 |
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| 91 | // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
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| 92 | var rootSymbol = new ProgramRootSymbol();
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| 93 | var startSymbol = new StartSymbol();
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| 94 | var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol); // this will change crossoverPoint0.Child.Parent
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| 95 | double min0 = 0.0, max0 = 0.0;
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| 96 | foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows)) {
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| 97 | if (min0 > v) min0 = v;
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| 98 | if (max0 < v) max0 = v;
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| 99 | }
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| 100 | crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore correct parent
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| 101 |
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| 102 | var weights = new List<double>();
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| 103 | foreach (var node in allowedBranches) {
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| 104 | var parent = node.Parent;
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| 105 | var tree1 = CreateTreeFromNode(random, node, rootSymbol, startSymbol);
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| 106 | double min1 = 0.0, max1 = 0.0;
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| 107 | foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows)) {
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| 108 | if (min1 > v) min1 = v;
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| 109 | if (max1 < v) max1 = v;
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| 110 | }
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| 111 | double behavioralDistance = (Math.Abs(min0 - min1) + Math.Abs(max0 - max1)) / 2; // this can be NaN of Infinity because some trees are crazy like exp(exp(exp(...))), we correct that below
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| 112 | weights.Add(behavioralDistance);
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| 113 | node.Parent = parent; // restore correct node parent
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| 114 | }
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| 115 |
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| 116 | // remove branches with an infinite or NaN behavioral distance
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[7494] | 117 | for (int i = weights.Count - 1; i >= 0; --i) {
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| 118 | if (Double.IsNaN(weights[i]) || Double.IsInfinity(weights[i])) {
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| 119 | weights.RemoveAt(i);
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| 120 | allowedBranches.RemoveAt(i);
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[7476] | 121 | }
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| 122 | }
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| 123 | // check if there are any allowed branches left
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| 124 | if (allowedBranches.Count == 0)
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| 125 | return parent0;
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| 126 |
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| 127 | ISymbolicExpressionTreeNode selectedBranch;
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| 128 | double sum = weights.Sum();
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| 129 |
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| 130 | if (sum.IsAlmost(0.0) || weights.Count == 1) // if there is only one allowed branch, or if all weights are zero
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| 131 | selectedBranch = allowedBranches[0];
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| 132 | else {
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| 133 | for (int i = 0; i != weights.Count; ++i) // normalize and invert values
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| 134 | weights[i] = 1 - weights[i] / sum;
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| 135 |
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| 136 | sum = weights.Sum(); // take new sum
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| 137 |
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| 138 | // compute the probabilities (selection weights)
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| 139 | for (int i = 0; i != weights.Count; ++i)
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| 140 | weights[i] /= sum;
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| 141 |
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[12422] | 142 | #pragma warning disable 612, 618
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[7476] | 143 | selectedBranch = allowedBranches.SelectRandom(weights, random);
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[12422] | 144 | #pragma warning restore 612, 618
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[7476] | 145 | }
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[7494] | 146 | Swap(crossoverPoint0, selectedBranch);
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[7476] | 147 | return parent0;
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| 148 | }
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| 149 | }
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| 150 | }
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