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source: branches/HeuristicLab.Crossovers/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionSemanticSimilarityCrossover.cs @ 7476

Last change on this file since 7476 was 7476, checked in by bburlacu, 12 years ago

#1682: Added crossovers.

File size: 7.1 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.Parameters;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Data;
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    protected override ISymbolicExpressionTree Cross(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    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
67      return Cross(random, parent0, parent1);
68    }
69
70    /// <summary>
71    /// Takes two parent individuals P0 and P1.
72    /// Randomly choose a node i from the first parent, then get a node j from the second parent that matches the semantic similarity criteria.
73    /// </summary>
74    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
75                                                T problemData, List<int> rows, int maxDepth, int maxLength, DoubleRange range) {
76      var crossoverPoints0 = new List<CutPoint>();
77      parent0.Root.ForEachNodePostfix((n) => {
78        if (n.Subtrees.Any() && n != parent0.Root)
79          foreach (var child in n.Subtrees)
80            crossoverPoints0.Add(new CutPoint(n, child));
81      });
82      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
83      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
84      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
85
86      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
87      parent1.Root.ForEachNodePostfix((n) => {
88        if (n.Subtrees.Any() && n != parent1.Root)
89          allowedBranches.AddRange(n.Subtrees.Where(s => crossoverPoint0.IsMatchingPointType(s) && s.GetDepth() + level <= maxDepth && s.GetLength() + length <= maxLength));
90      });
91
92      if (allowedBranches.Count == 0)
93        return parent0;
94
95      var dataset = problemData.Dataset;
96
97      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
98      var rootSymbol = new ProgramRootSymbol();
99      var startSymbol = new StartSymbol();
100      var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol);
101      List<double> estimatedValues0 = interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows).ToList();
102      crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore parent
103      ISymbolicExpressionTreeNode selectedBranch = null;
104
105      // pick the first node that fulfills the semantic similarity conditions
106      foreach (var node in allowedBranches) {
107        var parent = node.Parent;
108        var tree1 = CreateTreeFromNode(random, node, startSymbol, rootSymbol); // this will affect node.Parent
109        List<double> estimatedValues1 = interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows).ToList();
110        node.Parent = parent; // restore parent
111
112        OnlineCalculatorError errorState;
113        double ssd = OnlineMeanAbsoluteErrorCalculator.Calculate(estimatedValues0, estimatedValues1, out errorState);
114
115        if (range.Start > ssd || range.End < ssd)
116          continue;
117
118        selectedBranch = node;
119        break;
120      }
121
122      // perform the actual swap
123      if (selectedBranch != null)
124        swap(crossoverPoint0, selectedBranch);
125
126      return parent0;
127    }
128
129    private static void swap(CutPoint crossoverPoint, ISymbolicExpressionTreeNode selectedBranch) {
130      // perform the actual swap
131      if (crossoverPoint.Child != null) {
132        // manipulate the tree of parent0 in place
133        // replace the branch in tree0 with the selected branch from tree1
134        crossoverPoint.Parent.RemoveSubtree(crossoverPoint.ChildIndex);
135        if (selectedBranch != null) {
136          crossoverPoint.Parent.InsertSubtree(crossoverPoint.ChildIndex, selectedBranch);
137        }
138      } else {
139        // child is null (additional child should be added under the parent)
140        if (selectedBranch != null) {
141          crossoverPoint.Parent.AddSubtree(selectedBranch);
142        }
143      }
144    }
145  }
146}
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