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source: branches/2521_ProblemRefactoring/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs @ 17320

Last change on this file since 17320 was 17320, checked in by mkommend, 5 years ago

#2521: Added cancellation token to evaluate function of problems.

File size: 4.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 System.Collections.Generic;
25using System.Threading;
26using HEAL.Attic;
27using HeuristicLab.Common;
28using HeuristicLab.Core;
29using HeuristicLab.Encodings.BinaryVectorEncoding;
30using HeuristicLab.Optimization;
31
32namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
33  // This code is based off the publication
34  // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014
35  // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid
36  [StorableType("D5F1358D-C100-40CF-9BA5-E95F72F64D1A")]
37  internal sealed class EvaluationTracker : Item, ISingleObjectiveProblemDefinition<BinaryVectorEncoding, BinaryVector> {
38    [Storable]
39    private SingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem;
40    [Storable]
41    private int maxEvaluations;
42
43    #region Properties
44    [Storable]
45    public double BestQuality {
46      get;
47      private set;
48    }
49    [Storable]
50    public int Evaluations {
51      get;
52      private set;
53    }
54    [Storable]
55    public int BestFoundOnEvaluation {
56      get;
57      private set;
58    }
59    [Storable]
60    public BinaryVector BestSolution {
61      get;
62      private set;
63    }
64
65    public BinaryVectorEncoding Encoding {
66      get { return problem.Encoding; }
67    }
68    #endregion
69
70
71    [StorableConstructor]
72    private EvaluationTracker(StorableConstructorFlag _) : base(_) { }
73
74    private EvaluationTracker(EvaluationTracker original, Cloner cloner)
75      : base(original, cloner) {
76      problem = cloner.Clone(original.problem);
77      maxEvaluations = original.maxEvaluations;
78      BestQuality = original.BestQuality;
79      Evaluations = original.Evaluations;
80      BestFoundOnEvaluation = original.BestFoundOnEvaluation;
81      BestSolution = cloner.Clone(original.BestSolution);
82    }
83    public override IDeepCloneable Clone(Cloner cloner) {
84      return new EvaluationTracker(this, cloner);
85    }
86
87    public EvaluationTracker(SingleObjectiveProblem<BinaryVectorEncoding, BinaryVector> problem, int maxEvaluations) {
88      this.problem = problem;
89      this.maxEvaluations = maxEvaluations;
90      BestSolution = new BinaryVector(problem.Encoding.Length);
91      BestQuality = double.NaN;
92      Evaluations = 0;
93      BestFoundOnEvaluation = 0;
94    }
95
96    public double Evaluate(BinaryVector vector, IRandom random) {
97      return Evaluate(vector, random, CancellationToken.None);
98    }
99
100    public double Evaluate(BinaryVector vector, IRandom random, CancellationToken cancellationToken) {
101      if (Evaluations >= maxEvaluations) throw new OperationCanceledException("Maximum Evaluation Limit Reached");
102      Evaluations++;
103      double fitness = problem.Evaluate(vector, random);
104      if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality)) {
105        BestQuality = fitness;
106        BestSolution = (BinaryVector)vector.Clone();
107        BestFoundOnEvaluation = Evaluations;
108      }
109      return fitness;
110    }
111
112    public bool Maximization {
113      get {
114        if (problem == null) return false;
115        return problem.Maximization;
116      }
117    }
118
119    public bool IsBetter(double quality, double bestQuality) {
120      return problem.IsBetter(quality, bestQuality);
121    }
122
123    public void Analyze(BinaryVector[] individuals, double[] qualities, ResultCollection results, IRandom random) {
124      problem.Analyze(individuals, qualities, results, random);
125    }
126
127    public IEnumerable<BinaryVector> GetNeighbors(BinaryVector individual, IRandom random) {
128      return problem.GetNeighbors(individual, random);
129    }
130  }
131}
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