[11666] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16057] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[11838] | 4 | * and the BEACON Center for the Study of Evolution in Action.
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[11666] | 5 | *
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| 6 | * This file is part of HeuristicLab.
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| 7 | *
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| 8 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 9 | * it under the terms of the GNU General Public License as published by
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| 10 | * the Free Software Foundation, either version 3 of the License, or
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| 11 | * (at your option) any later version.
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| 12 | *
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| 13 | * HeuristicLab is distributed in the hope that it will be useful,
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| 14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 16 | * GNU General Public License for more details.
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| 17 | *
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| 18 | * You should have received a copy of the GNU General Public License
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| 19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 20 | */
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| 21 | #endregion
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| 22 |
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| 23 | using System;
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[11987] | 24 | using HeuristicLab.Common;
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| 25 | using HeuristicLab.Core;
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[11999] | 26 | using HeuristicLab.Data;
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[11987] | 27 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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[11999] | 28 | using HeuristicLab.Parameters;
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[15301] | 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[11987] | 30 | using HeuristicLab.Problems.Binary;
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[11666] | 31 |
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| 32 | namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
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[11838] | 33 | // This code is based off the publication
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| 34 | // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014
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| 35 | // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid
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[15301] | 36 | [StorableClass]
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[11987] | 37 | internal sealed class EvaluationTracker : BinaryProblem {
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[15301] | 38 | [Storable]
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[11987] | 39 | private readonly BinaryProblem problem;
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[15301] | 40 | [Storable]
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[11666] | 41 | private int maxEvaluations;
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| 42 |
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[11669] | 43 | #region Properties
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[15301] | 44 | [Storable]
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[11666] | 45 | public double BestQuality {
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[11669] | 46 | get;
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| 47 | private set;
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[11666] | 48 | }
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[15301] | 49 | [Storable]
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[11666] | 50 | public int Evaluations {
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[11669] | 51 | get;
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| 52 | private set;
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[11666] | 53 | }
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[15301] | 54 | [Storable]
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[11666] | 55 | public int BestFoundOnEvaluation {
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[11669] | 56 | get;
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| 57 | private set;
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[11666] | 58 | }
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[15301] | 59 | [Storable]
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[11987] | 60 | public BinaryVector BestSolution {
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[11669] | 61 | get;
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| 62 | private set;
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[11666] | 63 | }
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[11669] | 64 | #endregion
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[11666] | 65 |
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[15301] | 66 |
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| 67 | [StorableConstructor]
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| 68 | private EvaluationTracker(bool deserializing) : base(deserializing) { }
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| 69 |
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[11987] | 70 | private EvaluationTracker(EvaluationTracker original, Cloner cloner)
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| 71 | : base(original, cloner) {
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| 72 | problem = cloner.Clone(original.problem);
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| 73 | maxEvaluations = original.maxEvaluations;
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| 74 | BestQuality = original.BestQuality;
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| 75 | Evaluations = original.Evaluations;
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| 76 | BestFoundOnEvaluation = original.BestFoundOnEvaluation;
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[15301] | 77 | BestSolution = cloner.Clone(original.BestSolution);
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[11987] | 78 | }
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| 79 | public override IDeepCloneable Clone(Cloner cloner) {
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| 80 | return new EvaluationTracker(this, cloner);
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| 81 | }
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[15301] | 82 |
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[11987] | 83 | public EvaluationTracker(BinaryProblem problem, int maxEvaluations) {
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[11666] | 84 | this.problem = problem;
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| 85 | this.maxEvaluations = maxEvaluations;
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[11987] | 86 | BestSolution = new BinaryVector(Length);
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[11669] | 87 | BestQuality = double.NaN;
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| 88 | Evaluations = 0;
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| 89 | BestFoundOnEvaluation = 0;
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[11999] | 90 |
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| 91 | if (Parameters.ContainsKey("Maximization")) Parameters.Remove("Maximization");
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| 92 | Parameters.Add(new FixedValueParameter<BoolValue>("Maximization", "Set to false if the problem should be minimized.", (BoolValue)new BoolValue(Maximization).AsReadOnly()) { Hidden = true });
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[11666] | 93 | }
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| 94 |
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[11987] | 95 | public override double Evaluate(BinaryVector vector, IRandom random) {
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[11669] | 96 | if (Evaluations >= maxEvaluations) throw new OperationCanceledException("Maximum Evaluation Limit Reached");
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| 97 | Evaluations++;
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[11987] | 98 | double fitness = problem.Evaluate(vector, random);
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[11669] | 99 | if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality)) {
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| 100 | BestQuality = fitness;
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[11987] | 101 | BestSolution = (BinaryVector)vector.Clone();
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[11669] | 102 | BestFoundOnEvaluation = Evaluations;
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[11666] | 103 | }
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| 104 | return fitness;
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| 105 | }
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| 106 |
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[11987] | 107 | public override int Length {
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[11666] | 108 | get { return problem.Length; }
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[11987] | 109 | set { problem.Length = value; }
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[11666] | 110 | }
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[11987] | 111 |
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| 112 | public override bool Maximization {
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[11999] | 113 | get {
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| 114 | if (problem == null) return false;
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| 115 | return problem.Maximization;
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| 116 | }
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[11666] | 117 | }
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[11987] | 118 |
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[12057] | 119 | public override bool IsBetter(double quality, double bestQuality) {
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[11666] | 120 | return problem.IsBetter(quality, bestQuality);
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[11669] | 121 | }
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[11987] | 122 |
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[11666] | 123 | }
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| 124 | }
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