source: trunk/sources/HeuristicLab.Encodings.SymbolicExpressionTreeEncoding/3.4/Crossovers/SubtreeCrossover.cs @ 12509

Last change on this file since 12509 was 12509, checked in by mkommend, 6 years ago

#2276: Reintegrated branch for dataset refactoring.

File size: 10.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Random;
31
32namespace HeuristicLab.Encodings.SymbolicExpressionTreeEncoding {
33  /// <summary>
34  /// Takes two parent individuals P0 and P1 each. Selects a random node N0 of P0 and a random node N1 of P1.
35  /// And replaces the branch with root0 N0 in P0 with N1 from P1 if the tree-size limits are not violated.
36  /// When recombination with N0 and N1 would create a tree that is too large or invalid the operator randomly selects new N0 and N1
37  /// until a valid configuration is found.
38  /// </summary> 
39  [Item("SubtreeSwappingCrossover", "An operator which performs subtree swapping crossover.")]
40  [StorableClass]
41  public class SubtreeCrossover : SymbolicExpressionTreeCrossover, ISymbolicExpressionTreeSizeConstraintOperator {
42    private const string InternalCrossoverPointProbabilityParameterName = "InternalCrossoverPointProbability";
43    private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
44    private const string MaximumSymbolicExpressionTreeDepthParameterName = "MaximumSymbolicExpressionTreeDepth";
45
46    #region Parameter Properties
47    public IValueLookupParameter<PercentValue> InternalCrossoverPointProbabilityParameter {
48      get { return (IValueLookupParameter<PercentValue>)Parameters[InternalCrossoverPointProbabilityParameterName]; }
49    }
50    public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
51      get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
52    }
53    public IValueLookupParameter<IntValue> MaximumSymbolicExpressionTreeDepthParameter {
54      get { return (IValueLookupParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeDepthParameterName]; }
55    }
56    #endregion
57    #region Properties
58    public PercentValue InternalCrossoverPointProbability {
59      get { return InternalCrossoverPointProbabilityParameter.ActualValue; }
60    }
61    public IntValue MaximumSymbolicExpressionTreeLength {
62      get { return MaximumSymbolicExpressionTreeLengthParameter.ActualValue; }
63    }
64    public IntValue MaximumSymbolicExpressionTreeDepth {
65      get { return MaximumSymbolicExpressionTreeDepthParameter.ActualValue; }
66    }
67    #endregion
68    [StorableConstructor]
69    protected SubtreeCrossover(bool deserializing) : base(deserializing) { }
70    protected SubtreeCrossover(SubtreeCrossover original, Cloner cloner) : base(original, cloner) { }
71    public SubtreeCrossover()
72      : base() {
73      Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, "The maximal length (number of nodes) of the symbolic expression tree."));
74      Parameters.Add(new ValueLookupParameter<IntValue>(MaximumSymbolicExpressionTreeDepthParameterName, "The maximal depth of the symbolic expression tree (a tree with one node has depth = 0)."));
75      Parameters.Add(new ValueLookupParameter<PercentValue>(InternalCrossoverPointProbabilityParameterName, "The probability to select an internal crossover point (instead of a leaf node).", new PercentValue(0.9)));
76    }
77
78    public override IDeepCloneable Clone(Cloner cloner) {
79      return new SubtreeCrossover(this, cloner);
80    }
81
82    public override ISymbolicExpressionTree Crossover(IRandom random,
83      ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
84      return Cross(random, parent0, parent1, InternalCrossoverPointProbability.Value,
85        MaximumSymbolicExpressionTreeLength.Value, MaximumSymbolicExpressionTreeDepth.Value);
86    }
87
88    public static ISymbolicExpressionTree Cross(IRandom random,
89      ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1,
90      double internalCrossoverPointProbability, int maxTreeLength, int maxTreeDepth) {
91      // select a random crossover point in the first parent
92      CutPoint crossoverPoint0;
93      SelectCrossoverPoint(random, parent0, internalCrossoverPointProbability, maxTreeLength, maxTreeDepth, out crossoverPoint0);
94
95      int childLength = crossoverPoint0.Child != null ? crossoverPoint0.Child.GetLength() : 0;
96      // calculate the max length and depth that the inserted branch can have
97      int maxInsertedBranchLength = maxTreeLength - (parent0.Length - childLength);
98      int maxInsertedBranchDepth = maxTreeDepth - parent0.Root.GetBranchLevel(crossoverPoint0.Parent);
99
100      List<ISymbolicExpressionTreeNode> allowedBranches = new List<ISymbolicExpressionTreeNode>();
101      parent1.Root.ForEachNodePostfix((n) => {
102        if (n.GetLength() <= maxInsertedBranchLength &&
103            n.GetDepth() <= maxInsertedBranchDepth && crossoverPoint0.IsMatchingPointType(n))
104          allowedBranches.Add(n);
105      });
106      // empty branch
107      if (crossoverPoint0.IsMatchingPointType(null)) allowedBranches.Add(null);
108
109      if (allowedBranches.Count == 0) {
110        return parent0;
111      } else {
112        var selectedBranch = SelectRandomBranch(random, allowedBranches, internalCrossoverPointProbability);
113
114        if (crossoverPoint0.Child != null) {
115          // manipulate the tree of parent0 in place
116          // replace the branch in tree0 with the selected branch from tree1
117          crossoverPoint0.Parent.RemoveSubtree(crossoverPoint0.ChildIndex);
118          if (selectedBranch != null) {
119            crossoverPoint0.Parent.InsertSubtree(crossoverPoint0.ChildIndex, selectedBranch);
120          }
121        } else {
122          // child is null (additional child should be added under the parent)
123          if (selectedBranch != null) {
124            crossoverPoint0.Parent.AddSubtree(selectedBranch);
125          }
126        }
127        return parent0;
128      }
129    }
130
131    private static void SelectCrossoverPoint(IRandom random, ISymbolicExpressionTree parent0, double internalNodeProbability, int maxBranchLength, int maxBranchDepth, out CutPoint crossoverPoint) {
132      if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
133      List<CutPoint> internalCrossoverPoints = new List<CutPoint>();
134      List<CutPoint> leafCrossoverPoints = new List<CutPoint>();
135      parent0.Root.ForEachNodePostfix((n) => {
136        if (n.SubtreeCount > 0 && n != parent0.Root) {
137          //avoid linq to reduce memory pressure
138          for (int i = 0; i < n.SubtreeCount; i++) {
139            var child = n.GetSubtree(i);
140            if (child.GetLength() <= maxBranchLength &&
141                child.GetDepth() <= maxBranchDepth) {
142              if (child.SubtreeCount > 0)
143                internalCrossoverPoints.Add(new CutPoint(n, child));
144              else
145                leafCrossoverPoints.Add(new CutPoint(n, child));
146            }
147          }
148
149          // add one additional extension point if the number of sub trees for the symbol is not full
150          if (n.SubtreeCount < n.Grammar.GetMaximumSubtreeCount(n.Symbol)) {
151            // empty extension point
152            internalCrossoverPoints.Add(new CutPoint(n, n.SubtreeCount));
153          }
154        }
155      }
156    );
157
158      if (random.NextDouble() < internalNodeProbability) {
159        // select from internal node if possible
160        if (internalCrossoverPoints.Count > 0) {
161          // select internal crossover point or leaf
162          crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
163        } else {
164          // otherwise select external node
165          crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
166        }
167      } else if (leafCrossoverPoints.Count > 0) {
168        // select from leaf crossover point if possible
169        crossoverPoint = leafCrossoverPoints[random.Next(leafCrossoverPoints.Count)];
170      } else {
171        // otherwise select internal crossover point
172        crossoverPoint = internalCrossoverPoints[random.Next(internalCrossoverPoints.Count)];
173      }
174    }
175
176    private static ISymbolicExpressionTreeNode SelectRandomBranch(IRandom random, IEnumerable<ISymbolicExpressionTreeNode> branches, double internalNodeProbability) {
177      if (internalNodeProbability < 0.0 || internalNodeProbability > 1.0) throw new ArgumentException("internalNodeProbability");
178      List<ISymbolicExpressionTreeNode> allowedInternalBranches;
179      List<ISymbolicExpressionTreeNode> allowedLeafBranches;
180      if (random.NextDouble() < internalNodeProbability) {
181        // select internal node if possible
182        allowedInternalBranches = (from branch in branches
183                                   where branch != null && branch.SubtreeCount > 0
184                                   select branch).ToList();
185        if (allowedInternalBranches.Count > 0) {
186          return allowedInternalBranches.SampleRandom(random);
187
188        } else {
189          // no internal nodes allowed => select leaf nodes
190          allowedLeafBranches = (from branch in branches
191                                 where branch == null || branch.SubtreeCount == 0
192                                 select branch).ToList();
193          return allowedLeafBranches.SampleRandom(random);
194        }
195      } else {
196        // select leaf node if possible
197        allowedLeafBranches = (from branch in branches
198                               where branch == null || branch.SubtreeCount == 0
199                               select branch).ToList();
200        if (allowedLeafBranches.Count > 0) {
201          return allowedLeafBranches.SampleRandom(random);
202        } else {
203          allowedInternalBranches = (from branch in branches
204                                     where branch != null && branch.SubtreeCount > 0
205                                     select branch).ToList();
206          return allowedInternalBranches.SampleRandom(random);
207
208        }
209      }
210    }
211  }
212}
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