[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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[7476] | 29 |
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| 30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[7497] | 31 | [Item("DeterministicBestCrossover", "An operator which performs subtree swapping by choosing the best subtree to be swapped in a certain position:\n" +
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| 32 | "- Take two parent individuals P0 and P1\n" +
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| 33 | "- Randomly choose a crossover point C from P0\n" +
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| 34 | "- Test all nodes from P1 to determine the one that produces the best child when inserted at place C in P0")]
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[7476] | 35 | public sealed class SymbolicDataAnalysisExpressionDeterministicBestCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
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| 36 | [StorableConstructor]
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| 37 | private SymbolicDataAnalysisExpressionDeterministicBestCrossover(bool deserializing) : base(deserializing) { }
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| 38 | private SymbolicDataAnalysisExpressionDeterministicBestCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
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| 39 | : base(original, cloner) {
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| 40 | }
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| 41 | public SymbolicDataAnalysisExpressionDeterministicBestCrossover()
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| 42 | : base() {
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[7521] | 43 | name = "DeterministicBestCrossover";
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[7476] | 44 | }
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| 45 | public override IDeepCloneable Clone(Cloner cloner) {
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| 46 | return new SymbolicDataAnalysisExpressionDeterministicBestCrossover<T>(this, cloner);
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| 47 | }
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[7481] | 48 | public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
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[7476] | 49 | if (this.ExecutionContext == null)
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| 50 | throw new InvalidOperationException("ExecutionContext not set.");
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| 51 | List<int> rows = GenerateRowsToEvaluate().ToList();
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| 52 | T problemData = ProblemDataParameter.ActualValue;
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| 53 | ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator = EvaluatorParameter.ActualValue;
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| 54 |
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| 55 | return Cross(random, parent0, parent1, this.ExecutionContext, evaluator, problemData, 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 test all nodes j from the second parent to determine the best child that would be obtained by swapping i for j.
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| 61 | /// </summary>
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| 62 | public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, IExecutionContext context,
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| 63 | ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator, T problemData, List<int> rows, int maxDepth, int maxLength) {
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| 64 | var crossoverPoints0 = new List<CutPoint>();
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| 65 | parent0.Root.ForEachNodePostfix((n) => {
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[7497] | 66 | if (n.Parent != null && n.Parent != parent0.Root)
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| 67 | crossoverPoints0.Add(new CutPoint(n.Parent, n));
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[7476] | 68 | });
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| 69 | CutPoint crossoverPoint0 = crossoverPoints0.SelectRandom(random);
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| 70 | int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
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| 71 | int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
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| 72 |
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| 73 | var allowedBranches = new List<ISymbolicExpressionTreeNode>();
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| 74 | parent1.Root.ForEachNodePostfix((n) => {
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[7497] | 75 | if (n.Parent != null && n.Parent != parent1.Root) {
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| 76 | if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
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| 77 | allowedBranches.Add(n);
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| 78 | }
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[7476] | 79 | });
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| 80 |
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| 81 | if (allowedBranches.Count == 0)
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| 82 | return parent0;
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| 83 |
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| 84 | // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
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| 85 | ISymbolicExpressionTreeNode selectedBranch = null;
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| 86 | var nodeQualities = new List<Tuple<ISymbolicExpressionTreeNode, double>>();
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| 87 | var originalChild = crossoverPoint0.Child;
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| 88 |
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| 89 | foreach (var node in allowedBranches) {
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| 90 | var parent = node.Parent;
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[7497] | 91 | Swap(crossoverPoint0, node); // the swap will set the nodes parent to crossoverPoint0.Parent
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[7506] | 92 | IExecutionContext childContext = new ExecutionContext(context, evaluator, context.Scope);
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| 93 | double quality = evaluator.Evaluate(childContext, parent0, problemData, rows);
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[7497] | 94 | Swap(crossoverPoint0, originalChild); // swap the child back (so that the next swap will not affect the currently swapped node from parent1)
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[7476] | 95 | nodeQualities.Add(new Tuple<ISymbolicExpressionTreeNode, double>(node, quality));
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| 96 | node.Parent = parent; // restore correct parent
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| 97 | }
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| 98 |
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[7506] | 99 | nodeQualities.Sort((a, b) => a.Item2.CompareTo(b.Item2));
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[7476] | 100 | selectedBranch = evaluator.Maximization ? nodeQualities.Last().Item1 : nodeQualities.First().Item1;
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| 101 |
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| 102 | // swap the node that would create the best offspring
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[7497] | 103 | Swap(crossoverPoint0, selectedBranch);
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[7476] | 104 | return parent0;
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| 105 | }
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| 106 | }
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| 107 | }
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