source: branches/2974_Constants_Optimization/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Crossovers/SymbolicDataAnalysisExpressionDeterministicBestCrossover.cs @ 17193

Last change on this file since 17193 was 17193, checked in by mkommend, 2 months ago

#2974: Merged trunk changes into branch.

File size: 5.8 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HEAL.Attic;
29using HeuristicLab.Random;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
32  [Item("DeterministicBestCrossover", "An operator which performs subtree swapping by choosing the best subtree to be swapped in a certain position:\n" +
33                                      "- Take two parent individuals P0 and P1\n" +
34                                      "- Randomly choose a crossover point C from P0\n" +
35                                      "- Test all nodes from P1 to determine the one that produces the best child when inserted at place C in P0")]
36  [StorableType("BC019C69-AA9D-4E98-B366-274EFD7922C4")]
37  public sealed class SymbolicDataAnalysisExpressionDeterministicBestCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
38    [StorableConstructor]
39    private SymbolicDataAnalysisExpressionDeterministicBestCrossover(StorableConstructorFlag _) : base(_) { }
40    private SymbolicDataAnalysisExpressionDeterministicBestCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
41      : base(original, cloner) {
42    }
43    public SymbolicDataAnalysisExpressionDeterministicBestCrossover()
44      : base() {
45      name = "DeterministicBestCrossover";
46    }
47    public override IDeepCloneable Clone(Cloner cloner) {
48      return new SymbolicDataAnalysisExpressionDeterministicBestCrossover<T>(this, cloner);
49    }
50    public override ISymbolicExpressionTree Crossover(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1) {
51      if (this.ExecutionContext == null)
52        throw new InvalidOperationException("ExecutionContext not set.");
53      List<int> rows = GenerateRowsToEvaluate().ToList();
54      T problemData = ProblemDataParameter.ActualValue;
55      ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator = EvaluatorParameter.ActualValue;
56
57      return Cross(random, parent0, parent1, this.ExecutionContext, evaluator, problemData, rows, MaximumSymbolicExpressionTreeDepth.Value, MaximumSymbolicExpressionTreeLength.Value);
58    }
59
60    /// <summary>
61    /// Takes two parent individuals P0 and P1.
62    /// 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.
63    /// </summary>
64    public static ISymbolicExpressionTree Cross(IRandom random, ISymbolicExpressionTree parent0, ISymbolicExpressionTree parent1, IExecutionContext context,
65                                                ISymbolicDataAnalysisSingleObjectiveEvaluator<T> evaluator, T problemData, List<int> rows, int maxDepth, int maxLength) {
66      var crossoverPoints0 = new List<CutPoint>();
67      parent0.Root.ForEachNodePostfix((n) => {
68        if (n.Parent != null && n.Parent != parent0.Root)
69          crossoverPoints0.Add(new CutPoint(n.Parent, n));
70      });
71
72      CutPoint crossoverPoint0 = crossoverPoints0.SampleRandom(random);
73      int level = parent0.Root.GetBranchLevel(crossoverPoint0.Child);
74      int length = parent0.Root.GetLength() - crossoverPoint0.Child.GetLength();
75
76      var allowedBranches = new List<ISymbolicExpressionTreeNode>();
77      parent1.Root.ForEachNodePostfix((n) => {
78        if (n.Parent != null && n.Parent != parent1.Root) {
79          if (n.GetDepth() + level <= maxDepth && n.GetLength() + length <= maxLength && crossoverPoint0.IsMatchingPointType(n))
80            allowedBranches.Add(n);
81        }
82      });
83
84      if (allowedBranches.Count == 0)
85        return parent0;
86
87      // create symbols in order to improvize an ad-hoc tree so that the child can be evaluated
88      ISymbolicExpressionTreeNode selectedBranch = null;
89      var nodeQualities = new List<Tuple<ISymbolicExpressionTreeNode, double>>();
90      var originalChild = crossoverPoint0.Child;
91
92      foreach (var node in allowedBranches) {
93        var parent = node.Parent;
94        Swap(crossoverPoint0, node); // the swap will set the nodes parent to crossoverPoint0.Parent
95        IExecutionContext childContext = new ExecutionContext(context, evaluator, context.Scope);
96        double quality = evaluator.Evaluate(childContext, parent0, problemData, rows);
97        Swap(crossoverPoint0, originalChild); // swap the child back (so that the next swap will not affect the currently swapped node from parent1)
98        nodeQualities.Add(new Tuple<ISymbolicExpressionTreeNode, double>(node, quality));
99        node.Parent = parent; // restore correct parent
100      }
101
102      nodeQualities.Sort((a, b) => a.Item2.CompareTo(b.Item2));
103      selectedBranch = evaluator.Maximization ? nodeQualities.Last().Item1 : nodeQualities.First().Item1;
104
105      // swap the node that would create the best offspring
106      Swap(crossoverPoint0, selectedBranch);
107      return parent0;
108    }
109  }
110}
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