source: trunk/sources/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs @ 13395

Last change on this file since 13395 was 13395, checked in by pfleck, 7 years ago

#2525

  • Changed unit test that it also lists types that have a StorableConstructor but are not marked as StorableClass.
  • Put both storable tests in a single StorableTest.cs.
  • Added missing StorableClassAttributes.
File size: 3.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 * and the BEACON Center for the Study of Evolution in Action.
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21#endregion
22
23using System;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.BinaryVectorEncoding;
28using HeuristicLab.Parameters;
29using HeuristicLab.Problems.Binary;
30
31namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
32  // This code is based off the publication
33  // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014
34  // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid
35  internal sealed class EvaluationTracker : BinaryProblem {
36    private readonly BinaryProblem problem;
37
38    private int maxEvaluations;
39
40    #region Properties
41    public double BestQuality {
42      get;
43      private set;
44    }
45
46    public int Evaluations {
47      get;
48      private set;
49    }
50
51    public int BestFoundOnEvaluation {
52      get;
53      private set;
54    }
55
56    public BinaryVector BestSolution {
57      get;
58      private set;
59    }
60    #endregion
61
62    private EvaluationTracker(EvaluationTracker original, Cloner cloner)
63      : base(original, cloner) {
64      problem = cloner.Clone(original.problem);
65      maxEvaluations = original.maxEvaluations;
66      BestQuality = original.BestQuality;
67      Evaluations = original.Evaluations;
68      BestFoundOnEvaluation = original.BestFoundOnEvaluation;
69      BestSolution = cloner.Clone(BestSolution);
70    }
71    public override IDeepCloneable Clone(Cloner cloner) {
72      return new EvaluationTracker(this, cloner);
73    }
74    public EvaluationTracker(BinaryProblem problem, int maxEvaluations) {
75      this.problem = problem;
76      this.maxEvaluations = maxEvaluations;
77      BestSolution = new BinaryVector(Length);
78      BestQuality = double.NaN;
79      Evaluations = 0;
80      BestFoundOnEvaluation = 0;
81
82      if (Parameters.ContainsKey("Maximization")) Parameters.Remove("Maximization");
83      Parameters.Add(new FixedValueParameter<BoolValue>("Maximization", "Set to false if the problem should be minimized.", (BoolValue)new BoolValue(Maximization).AsReadOnly()) { Hidden = true });
84    }
85
86    public override double Evaluate(BinaryVector vector, IRandom random) {
87      if (Evaluations >= maxEvaluations) throw new OperationCanceledException("Maximum Evaluation Limit Reached");
88      Evaluations++;
89      double fitness = problem.Evaluate(vector, random);
90      if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality)) {
91        BestQuality = fitness;
92        BestSolution = (BinaryVector)vector.Clone();
93        BestFoundOnEvaluation = Evaluations;
94      }
95      return fitness;
96    }
97
98    public override int Length {
99      get { return problem.Length; }
100      set { problem.Length = value; }
101    }
102
103    public override bool Maximization {
104      get {
105        if (problem == null) return false;
106        return problem.Maximization;
107      }
108    }
109
110    public override bool IsBetter(double quality, double bestQuality) {
111      return problem.IsBetter(quality, bestQuality);
112    }
113
114  }
115}
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