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source: stable/HeuristicLab.Algorithms.ParameterlessPopulationPyramid/3.3/EvaluationTracker.cs @ 14330

Last change on this file since 14330 was 14186, checked in by swagner, 8 years ago

#2526: Updated year of copyrights in license headers

File size: 3.9 KB
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[11666]1#region License Information
2/* HeuristicLab
[14186]3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[11838]4 * and the BEACON Center for the Study of Evolution in Action.
[11666]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;
[12005]24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.BinaryVectorEncoding;
28using HeuristicLab.Parameters;
29using HeuristicLab.Problems.Binary;
[11666]30
31namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
[11838]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
[12005]35  internal sealed class EvaluationTracker : BinaryProblem {
36    private readonly BinaryProblem problem;
[11669]37
[11666]38    private int maxEvaluations;
39
[11669]40    #region Properties
[11666]41    public double BestQuality {
[11669]42      get;
43      private set;
[11666]44    }
45
46    public int Evaluations {
[11669]47      get;
48      private set;
[11666]49    }
50
51    public int BestFoundOnEvaluation {
[11669]52      get;
53      private set;
[11666]54    }
55
[12005]56    public BinaryVector BestSolution {
[11669]57      get;
58      private set;
[11666]59    }
[11669]60    #endregion
[11666]61
[12005]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) {
[11666]75      this.problem = problem;
76      this.maxEvaluations = maxEvaluations;
[12005]77      BestSolution = new BinaryVector(Length);
[11669]78      BestQuality = double.NaN;
79      Evaluations = 0;
80      BestFoundOnEvaluation = 0;
[12005]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 });
[11666]84    }
85
[12005]86    public override double Evaluate(BinaryVector vector, IRandom random) {
[11669]87      if (Evaluations >= maxEvaluations) throw new OperationCanceledException("Maximum Evaluation Limit Reached");
88      Evaluations++;
[12005]89      double fitness = problem.Evaluate(vector, random);
[11669]90      if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality)) {
91        BestQuality = fitness;
[12005]92        BestSolution = (BinaryVector)vector.Clone();
[11669]93        BestFoundOnEvaluation = Evaluations;
[11666]94      }
95      return fitness;
96    }
97
[12005]98    public override int Length {
[11666]99      get { return problem.Length; }
[12005]100      set { problem.Length = value; }
[11666]101    }
[12005]102
103    public override bool Maximization {
104      get {
105        if (problem == null) return false;
106        return problem.Maximization;
107      }
[11666]108    }
[12005]109
[12121]110    public override bool IsBetter(double quality, double bestQuality) {
[11666]111      return problem.IsBetter(quality, bestQuality);
[11669]112    }
[12005]113
[11666]114  }
115}
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