1 | #region License Information
|
---|
2 | /* HeuristicLab
|
---|
3 | * Copyright (C) 2002-2014 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 |
|
---|
22 | using System;
|
---|
23 | using System.Collections.Generic;
|
---|
24 | using System.Linq;
|
---|
25 | using HeuristicLab.Common;
|
---|
26 | using HeuristicLab.Core;
|
---|
27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
29 |
|
---|
30 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
|
---|
31 | [Item("ContextAwareCrossover", "An operator which deterministically choses the best insertion point for a randomly selected node:\n" +
|
---|
32 | "- Take two parent individuals P0 and P1\n" +
|
---|
33 | "- Randomly choose a node N from P1\n" +
|
---|
34 | "- Test all crossover points from P0 to determine the best location for N to be inserted")]
|
---|
35 | public sealed class SymbolicDataAnalysisExpressionContextAwareCrossover<T> : SymbolicDataAnalysisExpressionCrossover<T> where T : class, IDataAnalysisProblemData {
|
---|
36 | [StorableConstructor]
|
---|
37 | private SymbolicDataAnalysisExpressionContextAwareCrossover(bool deserializing) : base(deserializing) { }
|
---|
38 | private SymbolicDataAnalysisExpressionContextAwareCrossover(SymbolicDataAnalysisExpressionCrossover<T> original, Cloner cloner)
|
---|
39 | : base(original, cloner) {
|
---|
40 | }
|
---|
41 | public SymbolicDataAnalysisExpressionContextAwareCrossover()
|
---|
42 | : base() {
|
---|
43 | name = "ContextAwareCrossover";
|
---|
44 | }
|
---|
45 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
46 | return new SymbolicDataAnalysisExpressionContextAwareCrossover<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 second parent, then test all possible crossover points from the first parent to determine the best location for i to be inserted.
|
---|
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 | // randomly choose a node from the second parent
|
---|
65 | var possibleChildren = new List<ISymbolicExpressionTreeNode>();
|
---|
66 | parent1.Root.ForEachNodePostfix((n) => {
|
---|
67 | if (n.Parent != null && n.Parent != parent1.Root)
|
---|
68 | possibleChildren.Add(n);
|
---|
69 | });
|
---|
70 | var selectedChild = possibleChildren.SelectRandom(random);
|
---|
71 | var crossoverPoints = new List<CutPoint>();
|
---|
72 | var qualities = new List<Tuple<CutPoint, double>>();
|
---|
73 |
|
---|
74 | parent0.Root.ForEachNodePostfix((n) => {
|
---|
75 | if (n.Parent != null && n.Parent != parent0.Root) {
|
---|
76 | var totalDepth = parent0.Root.GetBranchLevel(n) + selectedChild.GetDepth();
|
---|
77 | var totalLength = parent0.Root.GetLength() - n.GetLength() + selectedChild.GetLength();
|
---|
78 | if (totalDepth <= maxDepth && totalLength <= maxLength) {
|
---|
79 | var crossoverPoint = new CutPoint(n.Parent, n);
|
---|
80 | if (crossoverPoint.IsMatchingPointType(selectedChild))
|
---|
81 | crossoverPoints.Add(crossoverPoint);
|
---|
82 | }
|
---|
83 | }
|
---|
84 | });
|
---|
85 |
|
---|
86 | if (crossoverPoints.Any()) {
|
---|
87 | // this loop will perform two swap operations per each crossover point
|
---|
88 | foreach (var crossoverPoint in crossoverPoints) {
|
---|
89 | // save the old parent so we can restore it later
|
---|
90 | var parent = selectedChild.Parent;
|
---|
91 | // perform a swap and check the quality of the solution
|
---|
92 | Swap(crossoverPoint, selectedChild);
|
---|
93 | IExecutionContext childContext = new ExecutionContext(context, evaluator, context.Scope);
|
---|
94 | double quality = evaluator.Evaluate(childContext, parent0, problemData, rows);
|
---|
95 | qualities.Add(new Tuple<CutPoint, double>(crossoverPoint, quality));
|
---|
96 | // restore the correct parent
|
---|
97 | selectedChild.Parent = parent;
|
---|
98 | // swap the replaced subtree back into the tree so that the structure is preserved
|
---|
99 | Swap(crossoverPoint, crossoverPoint.Child);
|
---|
100 | }
|
---|
101 |
|
---|
102 | qualities.Sort((a, b) => a.Item2.CompareTo(b.Item2)); // assuming this sorts the list in ascending order
|
---|
103 | var crossoverPoint0 = evaluator.Maximization ? qualities.Last().Item1 : qualities.First().Item1;
|
---|
104 | // swap the node that would create the best offspring
|
---|
105 | // this last swap makes a total of (2 * crossoverPoints.Count() + 1) swap operations.
|
---|
106 | Swap(crossoverPoint0, selectedChild);
|
---|
107 | }
|
---|
108 |
|
---|
109 | return parent0;
|
---|
110 | }
|
---|
111 | }
|
---|
112 | }
|
---|