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

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

#1682: Added crossovers.

File size: 7.9 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
31
32  [Item("ProbabilisticFunctionalCrossover", "An operator which performs subtree swapping based on the behavioral similarity between subtrees.")]
33  public sealed class SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
34    [StorableConstructor]
35    private SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(bool deserializing) : base(deserializing) { }
36    private SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
37      : base(original, cloner) { }
38    public SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover()
39      : base() {
40      Name = "ProbabilisticFunctionalCrossover";
41    }
42    public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicDataAnalysisExpressionProbabilisticFunctionalCrossover<T>(this, cloner); }
43
44    protected override ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
45      ISymbolicDataAnalysisExpressionTreeInterpreter interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
46      List<int> rows = GenerateRowsToEvaluate().ToList();
47      T problemData = ProblemDataParameter.ActualValue;
48      return Cross(random, parent0, parent1, interpreter, problemData,
49                   rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value);
50    }
51
52    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
53      return Cross(random, parent0, parent1);
54    }
55
56    /// <summary>
57    /// Takes two parent individuals P0 and P1.
58    /// 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:
59    /// d(i,j) = 0.5 * ( abs(max(i) - max(j)) + abs(min(i) - min(j)) ).
60    /// Next, assign probabilities for the selection of the second cross point based on the inversed and normalized behavioral distance and
61    /// choose the second crosspoint via a random weighted selection procedure.
62    /// </summary>
63    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
64                                                ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, T problemData, IList<int> rows, int maxDepth, int maxLength) {
65      var crossoverPoints0 = new List<CutPoint>();
66      parent0.Root.ForEachNodePostfix((n) => {
67        if (n.Subtrees.Any() && n != parent0.Root)
68          crossoverPoints0.AddRange(from s in n.Subtrees
69                                    select new CutPoint(n, s));
70      });
71      var crossoverPoint0 = crossoverPoints0.SelectRandom(random);
72      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
73      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
74
75      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
76      parent1.Root.ForEachNodePostfix((n) => {
77        if (n.Subtrees.Any() && n != parent1.Root)
78          allowedBranches.AddRange(from s in n.Subtrees
79                                   where crossoverPoint0.IsMatchingPointType(s) && s.GetDepth() + level <= maxDepth && s.GetLength() + length <= maxLength
80                                   select s);
81      });
82
83      if (allowedBranches.Count == 0)
84        return parent0;
85
86      var dataset = problemData.Dataset;
87
88      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
89      var rootSymbol = new ProgramRootSymbol();
90      var startSymbol = new StartSymbol();
91      var tree0 = CreateTreeFromNode(random, crossoverPoint0.Child, rootSymbol, startSymbol); // this will change crossoverPoint0.Child.Parent
92      double min0 = 0.0, max0 = 0.0;
93      foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree0, dataset, rows)) {
94        if (min0 > v) min0 = v;
95        if (max0 < v) max0 = v;
96      }
97      crossoverPoint0.Child.Parent = crossoverPoint0.Parent; // restore correct parent
98
99      var weights = new List<double>();
100      foreach (var node in allowedBranches) {
101        var parent = node.Parent;
102        var tree1 = CreateTreeFromNode(random, node, rootSymbol, startSymbol);
103        double min1 = 0.0, max1 = 0.0;
104        foreach (double v in interpreter.GetSymbolicExpressionTreeValues(tree1, dataset, rows)) {
105          if (min1 > v) min1 = v;
106          if (max1 < v) max1 = v;
107        }
108        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
109        weights.Add(behavioralDistance);
110        node.Parent = parent; // restore correct node parent
111      }
112
113      // remove branches with an infinite or NaN behavioral distance
114      int count = weights.Count, idx = 0;
115      while (idx < count) {
116        if (Double.IsNaN(weights[idx]) || Double.IsInfinity(weights[idx])) {
117          weights.RemoveAt(idx);
118          allowedBranches.RemoveAt(idx);
119          --count;
120        } else {
121          ++idx;
122        }
123      }
124
125      // check if there are any allowed branches left
126      if (allowedBranches.Count == 0)
127        return parent0;
128
129      ISymbolicExpressionTreeNode selectedBranch;
130      double sum = weights.Sum();
131
132      if (sum.IsAlmost(0.0) || weights.Count == 1) // if there is only one allowed branch, or if all weights are zero
133        selectedBranch = allowedBranches[0];
134      else {
135        for (int i = 0; i != weights.Count; ++i) // normalize and invert values
136          weights[i] = 1 - weights[i] / sum;
137
138        sum = weights.Sum(); // take new sum
139
140        // compute the probabilities (selection weights)
141        for (int i = 0; i != weights.Count; ++i)
142          weights[i] /= sum;
143
144        selectedBranch = allowedBranches.SelectRandom(weights, random);
145      }
146      swap(crossoverPoint0, selectedBranch);
147      return parent0;
148    }
149
150    private static void swap(CutPoint crossoverPoint, ISymbolicExpressionTreeNode selectedBranch) {
151      if (crossoverPoint.Child != null) {
152        // manipulate the tree of parent0 in place
153        // replace the branch in tree0 with the selected branch from tree1
154        crossoverPoint.Parent.RemoveSubtree(crossoverPoint.ChildIndex);
155        if (selectedBranch != null) {
156          crossoverPoint.Parent.InsertSubtree(crossoverPoint.ChildIndex, selectedBranch);
157        }
158      } else {
159        // child is null (additional child should be added under the parent)
160        if (selectedBranch != null) {
161          crossoverPoint.Parent.AddSubtree(selectedBranch);
162        }
163      }
164    }
165  }
166}
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