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source: branches/HeuristicLab.Crossovers/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs @ 7481

Last change on this file since 7481 was 7481, checked in by mkommend, 12 years ago

#1682: Corrected gp-crossover code.

  • Changed ISymbolicExpressionTreeCrossover
  • Corrected SubtreeCrossover
  • Updated MultiSymbolicDataAnalysisCrossover
File size: 6.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
32
33  [Item("SemanticSimilarityCrossover", "An operator which performs subtree swapping based on the semantic similarity between subtrees.")]
34  public sealed class SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
35    private const string SemanticSimilarityRangeParameterName = "SemanticSimilarityRange";
36
37    #region Parameter properties
38    public IValueParameter<DoubleRange> SemanticSimilarityRangeParameter {
39      get { return (IValueParameter<DoubleRange>)Parameters[SemanticSimilarityRangeParameterName]; }
40    }
41    #endregion
42
43    #region Properties
44    public DoubleRange SemanticSimilarityRange {
45      get { return SemanticSimilarityRangeParameter.Value; }
46    }
47    #endregion
48
49    [StorableConstructor]
50    private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(bool deserializing) : base(deserializing) { }
51    private SymbolicDataAnalysisExpressionSemanticSimilarityCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner) : base(original, cloner) { }
52    public SymbolicDataAnalysisExpressionSemanticSimilarityCrossover()
53      : base() {
54      Parameters.Add(new ValueLookupParameter<DoubleRange>(SemanticSimilarityRangeParameterName, "Semantic similarity interval.", new DoubleRange(0.0001, 10)));
55      Name = "SemanticSimilarityCrossover";
56    }
57    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicDataAnalysisExpressionSemanticSimilarityCrossover<T>(this, cloner); }
58
59    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
60      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
61      List<int> rows = GenerateRowsToEvaluate().ToList();
62      T problemData = ProblemDataParameter.ActualValue;
63      return Cross(random, parent0, parent1, interpreter, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value, SemanticSimilarityRange);
64    }
65
66    /// <summary>
67    /// Takes two parent individuals P0 and P1.
68    /// Randomly choose a node i from the first parent, then get a node j from the second parent that matches the semantic similarity criteria.
69    /// </summary>
70    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
71                                                T problemData, List<int> rows, int maxDepth, int maxLength, DoubleRange range) {
72      var crossoverPoints0 = new List<CutPoint>();
73      parent0.Root.ForEachNodePostfix((n) => {
74        if (n.Subtrees.Any() && n != parent0.Root)
75          foreach (var child in n.Subtrees)
76            crossoverPoints0.Add(new CutPoint(n, child));
77      });
78      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
79      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
80      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
81
82      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
83      parent1.Root.ForEachNodePostfix((n) => {
84        if (n.Subtrees.Any() && n != parent1.Root)
85          allowedBranches.AddRange(n.Subtrees.Where(s => crossoverPoint0.IsMatchingPointType(s) && s.GetDepth() + level <= maxDepth && s.GetLength() + length <= maxLength));
86      });
87
88      if (allowedBranches.Count == 0)
89        return parent0;
90
91      var dataset = problemData.Dataset;
92
93      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
94      var rootSymbol = new ProgramRootSymbol();
95      var startSymbol = new StartSymbol();
96      var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol);
97      List<double> estimatedValues0 = interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows).ToList();
98      crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore parent
99      ISymbolicExpressionTreeNode selectedBranch = null;
100
101      // pick the first node that fulfills the semantic similarity conditions
102      foreach (var node in allowedBranches) {
103        var parent = node.Parent;
104        var tree1 = CreateTreeFromNode(random, node, startSymbol, rootSymbol); // this will affect node.Parent
105        List<double> estimatedValues1 = interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows).ToList();
106        node.Parent = parent; // restore parent
107
108        OnlineCalculatorError errorState;
109        double ssd = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedValues0, estimatedValues1, out errorState);
110
111        if (range.Start > ssd || range.End < ssd)
112          continue;
113
114        selectedBranch = node;
115        break;
116      }
117
118      // perform the actual swap
119      if (selectedBranch != null)
120        swap(crossoverPoint0, selectedBranch);
121
122      return parent0;
123    }
124
125    private static void swap(CutPoint crossoverPoint, ISymbolicExpressionTreeNode selectedBranch) {
126      // perform the actual swap
127      if (crossoverPoint.Child != null) {
128        // manipulate the tree of parent0 in place
129        // replace the branch in tree0 with the selected branch from tree1
130        crossoverPoint.Parent.RemoveSubtree(crossoverPoint.ChildIndex);
131        if (selectedBranch != null) {
132          crossoverPoint.Parent.InsertSubtree(crossoverPoint.ChildIndex, selectedBranch);
133        }
134      } else {
135        // child is null (additional child should be added under the parent)
136        if (selectedBranch != null) {
137          crossoverPoint.Parent.AddSubtree(selectedBranch);
138        }
139      }
140    }
141  }
142}
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