source: branches/HeuristicLab.DataAnalysis.Symbolic.LinearInterpreter/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDeterministicBestCrossover.cs @ 9732

Last change on this file since 9732 was 9732, checked in by bburlacu, 6 years ago

#2021: Merged trunk changes for HeuristicLab.Encodings.SymbolicExpressionTreeEncoding and HeuristicLab.Problems.DataAnalysis.Symbolic. Replaced prefix iteration of nodes in the linear interpretation with breadth iteration for simplified logic and extra performance. Reversed unnecessary changes to other projects.

File size: 5.7 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
31  [Item("DeterministicBestCrossover", "An operator which performs subtree swapping by choosing the best subtree to be swapped in a certain position:\n" +
32                                      "- Take two parent individuals P0 and P1\n" +
33                                      "- Randomly choose a crossover point C from P0\n" +
34                                      "- Test all nodes from P1 to determine the one that produces the best child when inserted at place C in P0")]
35  public sealed class SymbolicDataAnalysisExpressionDeterministicBestCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
36    [StorableConstructor]
37    private SymbolicDataAnalysisExpressionDeterministicBestCrossover(bool deserializing) : base(deserializing) { }
38    private SymbolicDataAnalysisExpressionDeterministicBestCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
39      : base(original, cloner) {
40    }
41    public SymbolicDataAnalysisExpressionDeterministicBestCrossover()
42      : base() {
43      name = "DeterministicBestCrossover";
44    }
45    public override IDeepCloneable Clone(Cloner cloner) {
46      return new SymbolicDataAnalysisExpressionDeterministicBestCrossover<T>(this, cloner);
47    }
48    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
49      if (this.ExecutionContext == null)
50        throw new InvalidOperationException("ExecutionContext not set.");
51      List<int> rows = GenerateRowsToEvaluate().ToList();
52      T problemData = ProblemDataParameter.ActualValue;
53      ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator = EvaluatorParameter.ActualValue;
54
55      return Cross(random, parent0, parent1, this.ExecutionContext, evaluator, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value);
56    }
57
58    /// <summary>
59    /// Takes two parent individuals P0 and P1.
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.
61    /// </summary>
62    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, IExecutionContext context,
63                                                ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator, T problemData, List<int> rows, int maxDepth, int maxLength) {
64      var crossoverPoints0 = new List<CutPoint>();
65      parent0.Root.ForEachNodePostfix((n) => {
66        if (n.Parent != null && n.Parent != parent0.Root)
67          crossoverPoints0.Add(new CutPoint(n.Parent, n));
68      });
69      CutPoint crossoverPoint0 = crossoverPoints0.SelectRandom(random);
70      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
71      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
72
73      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
74      parent1.Root.ForEachNodePostfix((n) => {
75        if (n.Parent != null && n.Parent != parent1.Root) {
76          if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
77            allowedBranches.Add(n);
78        }
79      });
80
81      if (allowedBranches.Count == 0)
82        return parent0;
83
84      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
85      ISymbolicExpressionTreeNode selectedBranch = null;
86      var nodeQualities = new List<Tuple<ISymbolicExpressionTreeNode, double>>();
87      var originalChild = crossoverPoint0.Child;
88
89      foreach (var node in allowedBranches) {
90        var parent = node.Parent;
91        Swap(crossoverPoint0, node); // the swap will set the nodes parent to crossoverPoint0.Parent
92        IExecutionContext childContext = new ExecutionContext(context, evaluator, context.Scope);
93        double quality = evaluator.Evaluate(childContext, parent0, problemData, rows);
94        Swap(crossoverPoint0, originalChild); // swap the child back (so that the next swap will not affect the currently swapped node from parent1)
95        nodeQualities.Add(new Tuple<ISymbolicExpressionTreeNode, double>(node, quality));
96        node.Parent = parent; // restore correct parent
97      }
98
99      nodeQualities.Sort((a, b) => a.Item2.CompareTo(b.Item2));
100      selectedBranch = evaluator.Maximization ? nodeQualities.Last().Item1 : nodeQualities.First().Item1;
101
102      // swap the node that would create the best offspring
103      Swap(crossoverPoint0, selectedBranch);
104      return parent0;
105    }
106  }
107}
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