[11664] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[14185] | 3 | * Copyright (C) 2002-2016 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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| 5 | *
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[11664] | 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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| 24 | using System.Collections.Generic;
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[15310] | 25 | using System.Linq;
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[11791] | 26 | using System.Threading;
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[11666] | 27 | using HeuristicLab.Analysis;
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[11664] | 28 | using HeuristicLab.Common;
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| 29 | using HeuristicLab.Core;
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| 30 | using HeuristicLab.Data;
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[11666] | 31 | using HeuristicLab.Encodings.BinaryVectorEncoding;
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[11664] | 32 | using HeuristicLab.Optimization;
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| 33 | using HeuristicLab.Parameters;
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| 34 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[11987] | 35 | using HeuristicLab.Problems.Binary;
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[11664] | 36 | using HeuristicLab.Random;
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| 37 |
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| 38 | namespace HeuristicLab.Algorithms.ParameterlessPopulationPyramid {
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[11838] | 39 | // This code is based off the publication
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| 40 | // B. W. Goldman and W. F. Punch, "Parameter-less Population Pyramid," GECCO, pp. 785–792, 2014
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| 41 | // and the original source code in C++11 available from: https://github.com/brianwgoldman/Parameter-less_Population_Pyramid
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[13173] | 42 | [Item("Parameter-less Population Pyramid (P3)", "Binary value optimization algorithm which requires no configuration. B. W. Goldman and W. F. Punch, Parameter-less Population Pyramid, GECCO, pp. 785–792, 2014")]
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[11664] | 43 | [StorableClass]
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[13173] | 44 | [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 400)]
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[11791] | 45 | public class ParameterlessPopulationPyramid : BasicAlgorithm {
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| 46 | public override Type ProblemType {
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[11987] | 47 | get { return typeof(BinaryProblem); }
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[11791] | 48 | }
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[11987] | 49 | public new BinaryProblem Problem {
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| 50 | get { return (BinaryProblem)base.Problem; }
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[11791] | 51 | set { base.Problem = value; }
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| 52 | }
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[11667] | 53 |
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[15310] | 54 | [Storable]
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[11666] | 55 | private readonly IRandom random = new MersenneTwister();
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[15310] | 56 | [Storable]
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| 57 | private List<Population> pyramid = new List<Population>();
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| 58 | [Storable]
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[11666] | 59 | private EvaluationTracker tracker;
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[11664] | 60 |
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| 61 | // Tracks all solutions in Pyramid for quick membership checks
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[11681] | 62 |
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[15310] | 63 | private HashSet<BinaryVector> seen = new HashSet<BinaryVector>(new EnumerableBoolEqualityComparer());
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| 64 | [Storable]
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| 65 | private IEnumerable<BinaryVector> StorableSeen {
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| 66 | get { return seen; }
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| 67 | set { seen = new HashSet<BinaryVector>(value, new EnumerableBoolEqualityComparer()); }
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| 68 | }
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| 69 |
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[11669] | 70 | #region ParameterNames
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[11666] | 71 | private const string MaximumIterationsParameterName = "Maximum Iterations";
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[11669] | 72 | private const string MaximumEvaluationsParameterName = "Maximum Evaluations";
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[11791] | 73 | private const string MaximumRuntimeParameterName = "Maximum Runtime";
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[11669] | 74 | private const string SeedParameterName = "Seed";
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| 75 | private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
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| 76 | #endregion
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[11681] | 77 |
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[11669] | 78 | #region ParameterProperties
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[11666] | 79 | public IFixedValueParameter<IntValue> MaximumIterationsParameter {
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| 80 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumIterationsParameterName]; }
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[11664] | 81 | }
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[11669] | 82 | public IFixedValueParameter<IntValue> MaximumEvaluationsParameter {
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| 83 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumEvaluationsParameterName]; }
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| 84 | }
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[11791] | 85 | public IFixedValueParameter<IntValue> MaximumRuntimeParameter {
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| 86 | get { return (IFixedValueParameter<IntValue>)Parameters[MaximumRuntimeParameterName]; }
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| 87 | }
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[11669] | 88 | public IFixedValueParameter<IntValue> SeedParameter {
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| 89 | get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
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| 90 | }
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| 91 | public FixedValueParameter<BoolValue> SetSeedRandomlyParameter {
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| 92 | get { return (FixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
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| 93 | }
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| 94 | #endregion
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[11667] | 95 |
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[11669] | 96 | #region Properties
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[11666] | 97 | public int MaximumIterations {
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| 98 | get { return MaximumIterationsParameter.Value.Value; }
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| 99 | set { MaximumIterationsParameter.Value.Value = value; }
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[11664] | 100 | }
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[11666] | 101 | public int MaximumEvaluations {
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| 102 | get { return MaximumEvaluationsParameter.Value.Value; }
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| 103 | set { MaximumEvaluationsParameter.Value.Value = value; }
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| 104 | }
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[11791] | 105 | public int MaximumRuntime {
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| 106 | get { return MaximumRuntimeParameter.Value.Value; }
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| 107 | set { MaximumRuntimeParameter.Value.Value = value; }
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| 108 | }
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[11666] | 109 | public int Seed {
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| 110 | get { return SeedParameter.Value.Value; }
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| 111 | set { SeedParameter.Value.Value = value; }
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| 112 | }
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| 113 | public bool SetSeedRandomly {
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| 114 | get { return SetSeedRandomlyParameter.Value.Value; }
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| 115 | set { SetSeedRandomlyParameter.Value.Value = value; }
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| 116 | }
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[11669] | 117 | #endregion
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[11666] | 118 |
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| 119 | #region ResultsProperties
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| 120 | private double ResultsBestQuality {
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| 121 | get { return ((DoubleValue)Results["Best Quality"].Value).Value; }
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| 122 | set { ((DoubleValue)Results["Best Quality"].Value).Value = value; }
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| 123 | }
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| 124 |
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| 125 | private BinaryVector ResultsBestSolution {
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| 126 | get { return (BinaryVector)Results["Best Solution"].Value; }
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| 127 | set { Results["Best Solution"].Value = value; }
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| 128 | }
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| 129 |
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| 130 | private int ResultsBestFoundOnEvaluation {
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| 131 | get { return ((IntValue)Results["Evaluation Best Solution Was Found"].Value).Value; }
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| 132 | set { ((IntValue)Results["Evaluation Best Solution Was Found"].Value).Value = value; }
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| 133 | }
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| 134 |
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| 135 | private int ResultsEvaluations {
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| 136 | get { return ((IntValue)Results["Evaluations"].Value).Value; }
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| 137 | set { ((IntValue)Results["Evaluations"].Value).Value = value; }
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| 138 | }
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| 139 | private int ResultsIterations {
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| 140 | get { return ((IntValue)Results["Iterations"].Value).Value; }
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| 141 | set { ((IntValue)Results["Iterations"].Value).Value = value; }
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| 142 | }
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| 143 |
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| 144 | private DataTable ResultsQualities {
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| 145 | get { return ((DataTable)Results["Qualities"].Value); }
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| 146 | }
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| 147 | private DataRow ResultsQualitiesBest {
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| 148 | get { return ResultsQualities.Rows["Best Quality"]; }
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| 149 | }
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| 150 |
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| 151 | private DataRow ResultsQualitiesIteration {
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| 152 | get { return ResultsQualities.Rows["Iteration Quality"]; }
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| 153 | }
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[11681] | 154 |
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| 155 |
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| 156 | private DataRow ResultsLevels {
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| 157 | get { return ((DataTable)Results["Pyramid Levels"].Value).Rows["Levels"]; }
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| 158 | }
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| 159 |
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| 160 | private DataRow ResultsSolutions {
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| 161 | get { return ((DataTable)Results["Stored Solutions"].Value).Rows["Solutions"]; }
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| 162 | }
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[11666] | 163 | #endregion
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| 164 |
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[14523] | 165 | public override bool SupportsPause { get { return true; } }
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| 166 |
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[11664] | 167 | [StorableConstructor]
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| 168 | protected ParameterlessPopulationPyramid(bool deserializing) : base(deserializing) { }
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| 169 |
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| 170 | protected ParameterlessPopulationPyramid(ParameterlessPopulationPyramid original, Cloner cloner)
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| 171 | : base(original, cloner) {
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[15310] | 172 | random = cloner.Clone(original.random);
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| 173 | pyramid = original.pyramid.Select(cloner.Clone).ToList();
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| 174 | tracker = cloner.Clone(original.tracker);
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| 175 | seen = new HashSet<BinaryVector>(original.seen.Select(cloner.Clone), new EnumerableBoolEqualityComparer());
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[11664] | 176 | }
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| 177 |
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| 178 | public override IDeepCloneable Clone(Cloner cloner) {
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| 179 | return new ParameterlessPopulationPyramid(this, cloner);
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| 180 | }
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| 181 |
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[15310] | 182 | public ParameterlessPopulationPyramid() : base() {
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[11668] | 183 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumIterationsParameterName, "", new IntValue(Int32.MaxValue)));
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[11791] | 184 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumEvaluationsParameterName, "", new IntValue(Int32.MaxValue)));
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| 185 | Parameters.Add(new FixedValueParameter<IntValue>(MaximumRuntimeParameterName, "The maximum runtime in seconds after which the algorithm stops. Use -1 to specify no limit for the runtime", new IntValue(3600)));
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[11666] | 186 | Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
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| 187 | Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
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[11664] | 188 | }
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| 189 |
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[11791] | 190 | protected override void OnExecutionTimeChanged() {
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| 191 | base.OnExecutionTimeChanged();
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| 192 | if (CancellationTokenSource == null) return;
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| 193 | if (MaximumRuntime == -1) return;
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| 194 | if (ExecutionTime.TotalSeconds > MaximumRuntime) CancellationTokenSource.Cancel();
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| 195 | }
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| 196 |
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[11987] | 197 | private void AddIfUnique(BinaryVector solution, int level) {
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[11664] | 198 | // Don't add things you have seen
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| 199 | if (seen.Contains(solution)) return;
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| 200 | if (level == pyramid.Count) {
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[11666] | 201 | pyramid.Add(new Population(tracker.Length, random));
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[11664] | 202 | }
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[11987] | 203 | var copied = (BinaryVector)solution.Clone();
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[11667] | 204 | pyramid[level].Add(copied);
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| 205 | seen.Add(copied);
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[11664] | 206 | }
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| 207 |
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[11672] | 208 | // In the GECCO paper, Figure 1
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[11664] | 209 | private double iterate() {
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| 210 | // Create a random solution
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[11987] | 211 | BinaryVector solution = new BinaryVector(tracker.Length);
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[11664] | 212 | for (int i = 0; i < solution.Length; i++) {
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| 213 | solution[i] = random.Next(2) == 1;
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| 214 | }
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[11987] | 215 | double fitness = tracker.Evaluate(solution, random);
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[11666] | 216 | fitness = HillClimber.ImproveToLocalOptimum(tracker, solution, fitness, random);
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[11664] | 217 | AddIfUnique(solution, 0);
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[11667] | 218 |
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[11664] | 219 | for (int level = 0; level < pyramid.Count; level++) {
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| 220 | var current = pyramid[level];
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[11666] | 221 | double newFitness = LinkageCrossover.ImproveUsingTree(current.Tree, current.Solutions, solution, fitness, tracker, random);
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[11664] | 222 | // add it to the next level if its a strict fitness improvement
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[11666] | 223 | if (tracker.IsBetter(newFitness, fitness)) {
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[11664] | 224 | fitness = newFitness;
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| 225 | AddIfUnique(solution, level + 1);
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| 226 | }
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| 227 | }
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| 228 | return fitness;
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| 229 | }
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| 230 |
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[14517] | 231 | protected override void Initialize(CancellationToken cancellationToken) {
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[11669] | 232 | // Set up the algorithm
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[11666] | 233 | if (SetSeedRandomly) Seed = new System.Random().Next();
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[11664] | 234 | pyramid = new List<Population>();
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[11667] | 235 | seen.Clear();
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[11666] | 236 | random.Reset(Seed);
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| 237 | tracker = new EvaluationTracker(Problem, MaximumEvaluations);
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[11669] | 238 |
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| 239 | // Set up the results display
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[11666] | 240 | Results.Add(new Result("Iterations", new IntValue(0)));
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| 241 | Results.Add(new Result("Evaluations", new IntValue(0)));
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| 242 | Results.Add(new Result("Best Solution", new BinaryVector(tracker.BestSolution)));
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| 243 | Results.Add(new Result("Best Quality", new DoubleValue(tracker.BestQuality)));
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| 244 | Results.Add(new Result("Evaluation Best Solution Was Found", new IntValue(tracker.BestFoundOnEvaluation)));
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| 245 | var table = new DataTable("Qualities");
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| 246 | table.Rows.Add(new DataRow("Best Quality"));
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| 247 | var iterationRows = new DataRow("Iteration Quality");
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| 248 | iterationRows.VisualProperties.LineStyle = DataRowVisualProperties.DataRowLineStyle.Dot;
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| 249 | table.Rows.Add(iterationRows);
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| 250 | Results.Add(new Result("Qualities", table));
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[11669] | 251 |
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[11681] | 252 | table = new DataTable("Pyramid Levels");
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| 253 | table.Rows.Add(new DataRow("Levels"));
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| 254 | Results.Add(new Result("Pyramid Levels", table));
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| 255 |
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| 256 | table = new DataTable("Stored Solutions");
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| 257 | table.Rows.Add(new DataRow("Solutions"));
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| 258 | Results.Add(new Result("Stored Solutions", table));
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| 259 |
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[14517] | 260 | base.Initialize(cancellationToken);
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| 261 | }
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| 262 |
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| 263 | protected override void Run(CancellationToken cancellationToken) {
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[11669] | 264 | // Loop until iteration limit reached or canceled.
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[14517] | 265 | while (ResultsIterations < MaximumIterations) {
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[14527] | 266 | double fitness = double.NaN;
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[11666] | 267 |
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[14527] | 268 | try {
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| 269 | fitness = iterate();
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| 270 | ResultsIterations++;
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| 271 | cancellationToken.ThrowIfCancellationRequested();
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[15310] | 272 | }
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| 273 | finally {
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[14527] | 274 | ResultsEvaluations = tracker.Evaluations;
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| 275 | ResultsBestSolution = new BinaryVector(tracker.BestSolution);
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| 276 | ResultsBestQuality = tracker.BestQuality;
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| 277 | ResultsBestFoundOnEvaluation = tracker.BestFoundOnEvaluation;
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| 278 | ResultsQualitiesBest.Values.Add(tracker.BestQuality);
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| 279 | ResultsQualitiesIteration.Values.Add(fitness);
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| 280 | ResultsLevels.Values.Add(pyramid.Count);
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| 281 | ResultsSolutions.Values.Add(seen.Count);
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| 282 | }
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[11664] | 283 | }
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| 284 | }
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| 285 | }
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| 286 | }
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