[14420] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.ComponentModel;
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| 25 | using System.Linq;
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| 26 | using System.Threading;
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[14450] | 27 | using HeuristicLab.Algorithms.MemPR.Interfaces;
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[14420] | 28 | using HeuristicLab.Analysis;
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| 29 | using HeuristicLab.Common;
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| 30 | using HeuristicLab.Core;
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| 31 | using HeuristicLab.Data;
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| 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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[14544] | 35 | using HeuristicLab.Random;
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[14420] | 36 |
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| 37 | namespace HeuristicLab.Algorithms.MemPR {
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| 38 | [Item("MemPR Algorithm", "Base class for MemPR algorithms")]
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| 39 | [StorableClass]
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[14450] | 40 | public abstract class MemPRAlgorithm<TProblem, TSolution, TPopulationContext, TSolutionContext> : BasicAlgorithm, INotifyPropertyChanged
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| 41 | where TProblem : class, IItem, ISingleObjectiveHeuristicOptimizationProblem, ISingleObjectiveProblemDefinition
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[14420] | 42 | where TSolution : class, IItem
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[14450] | 43 | where TPopulationContext : MemPRPopulationContext<TProblem, TSolution, TPopulationContext, TSolutionContext>, new()
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| 44 | where TSolutionContext : MemPRSolutionContext<TProblem, TSolution, TPopulationContext, TSolutionContext> {
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[14420] | 45 | private const double MutationProbabilityMagicConst = 0.1;
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| 46 |
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| 47 | public override Type ProblemType {
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[14450] | 48 | get { return typeof(TProblem); }
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[14420] | 49 | }
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| 50 |
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[14450] | 51 | public new TProblem Problem {
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| 52 | get { return (TProblem)base.Problem; }
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[14420] | 53 | set { base.Problem = value; }
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| 54 | }
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| 55 |
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| 56 | protected string QualityName {
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| 57 | get { return Problem != null && Problem.Evaluator != null ? Problem.Evaluator.QualityParameter.ActualName : null; }
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| 58 | }
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| 59 |
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| 60 | public int? MaximumEvaluations {
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| 61 | get {
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| 62 | var val = ((OptionalValueParameter<IntValue>)Parameters["MaximumEvaluations"]).Value;
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| 63 | return val != null ? val.Value : (int?)null;
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| 64 | }
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| 65 | set {
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| 66 | var param = (OptionalValueParameter<IntValue>)Parameters["MaximumEvaluations"];
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| 67 | param.Value = value.HasValue ? new IntValue(value.Value) : null;
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| 68 | }
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| 69 | }
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| 70 |
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| 71 | public TimeSpan? MaximumExecutionTime {
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| 72 | get {
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| 73 | var val = ((OptionalValueParameter<TimeSpanValue>)Parameters["MaximumExecutionTime"]).Value;
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| 74 | return val != null ? val.Value : (TimeSpan?)null;
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| 75 | }
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| 76 | set {
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| 77 | var param = (OptionalValueParameter<TimeSpanValue>)Parameters["MaximumExecutionTime"];
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| 78 | param.Value = value.HasValue ? new TimeSpanValue(value.Value) : null;
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| 79 | }
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| 80 | }
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| 81 |
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| 82 | public double? TargetQuality {
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| 83 | get {
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| 84 | var val = ((OptionalValueParameter<DoubleValue>)Parameters["TargetQuality"]).Value;
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| 85 | return val != null ? val.Value : (double?)null;
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| 86 | }
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| 87 | set {
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| 88 | var param = (OptionalValueParameter<DoubleValue>)Parameters["TargetQuality"];
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| 89 | param.Value = value.HasValue ? new DoubleValue(value.Value) : null;
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| 90 | }
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| 91 | }
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| 92 |
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| 93 | protected FixedValueParameter<IntValue> MaximumPopulationSizeParameter {
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| 94 | get { return ((FixedValueParameter<IntValue>)Parameters["MaximumPopulationSize"]); }
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| 95 | }
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| 96 | public int MaximumPopulationSize {
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| 97 | get { return MaximumPopulationSizeParameter.Value.Value; }
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| 98 | set { MaximumPopulationSizeParameter.Value.Value = value; }
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| 99 | }
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| 100 |
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| 101 | public bool SetSeedRandomly {
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| 102 | get { return ((FixedValueParameter<BoolValue>)Parameters["SetSeedRandomly"]).Value.Value; }
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| 103 | set { ((FixedValueParameter<BoolValue>)Parameters["SetSeedRandomly"]).Value.Value = value; }
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| 104 | }
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| 105 |
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| 106 | public int Seed {
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| 107 | get { return ((FixedValueParameter<IntValue>)Parameters["Seed"]).Value.Value; }
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| 108 | set { ((FixedValueParameter<IntValue>)Parameters["Seed"]).Value.Value = value; }
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| 109 | }
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| 110 |
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| 111 | public IAnalyzer Analyzer {
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| 112 | get { return ((ValueParameter<IAnalyzer>)Parameters["Analyzer"]).Value; }
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| 113 | set { ((ValueParameter<IAnalyzer>)Parameters["Analyzer"]).Value = value; }
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| 114 | }
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| 115 |
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[14450] | 116 | public IConstrainedValueParameter<ISolutionModelTrainer<TPopulationContext>> SolutionModelTrainerParameter {
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| 117 | get { return (IConstrainedValueParameter<ISolutionModelTrainer<TPopulationContext>>)Parameters["SolutionModelTrainer"]; }
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[14420] | 118 | }
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| 119 |
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[14450] | 120 | public IConstrainedValueParameter<ILocalSearch<TSolutionContext>> LocalSearchParameter {
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| 121 | get { return (IConstrainedValueParameter<ILocalSearch<TSolutionContext>>)Parameters["LocalSearch"]; }
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[14420] | 122 | }
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| 123 |
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| 124 | [Storable]
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[14450] | 125 | private TPopulationContext context;
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| 126 | public TPopulationContext Context {
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[14420] | 127 | get { return context; }
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| 128 | protected set {
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| 129 | if (context == value) return;
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| 130 | context = value;
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| 131 | OnPropertyChanged("State");
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| 132 | }
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| 133 | }
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| 134 |
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| 135 | [Storable]
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| 136 | private BestAverageWorstQualityAnalyzer qualityAnalyzer;
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| 137 |
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| 138 | [StorableConstructor]
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| 139 | protected MemPRAlgorithm(bool deserializing) : base(deserializing) { }
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[14450] | 140 | protected MemPRAlgorithm(MemPRAlgorithm<TProblem, TSolution, TPopulationContext, TSolutionContext> original, Cloner cloner) : base(original, cloner) {
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[14420] | 141 | context = cloner.Clone(original.context);
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| 142 | qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
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| 143 | RegisterEventHandlers();
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| 144 | }
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| 145 | protected MemPRAlgorithm() {
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| 146 | Parameters.Add(new ValueParameter<IAnalyzer>("Analyzer", "The analyzer to apply to the population.", new MultiAnalyzer()));
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| 147 | Parameters.Add(new FixedValueParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population that is evolved.", new IntValue(20)));
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| 148 | Parameters.Add(new OptionalValueParameter<IntValue>("MaximumEvaluations", "The maximum number of solution evaluations."));
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| 149 | Parameters.Add(new OptionalValueParameter<TimeSpanValue>("MaximumExecutionTime", "The maximum runtime.", new TimeSpanValue(TimeSpan.FromMinutes(1))));
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| 150 | Parameters.Add(new OptionalValueParameter<DoubleValue>("TargetQuality", "The target quality at which the algorithm terminates."));
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| 151 | Parameters.Add(new FixedValueParameter<BoolValue>("SetSeedRandomly", "Whether each run of the algorithm should be conducted with a new random seed.", new BoolValue(true)));
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| 152 | Parameters.Add(new FixedValueParameter<IntValue>("Seed", "The random number seed that is used in case SetSeedRandomly is false.", new IntValue(0)));
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[14450] | 153 | Parameters.Add(new ConstrainedValueParameter<ISolutionModelTrainer<TPopulationContext>>("SolutionModelTrainer", "The object that creates a solution model that can be sampled."));
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| 154 | Parameters.Add(new ConstrainedValueParameter<ILocalSearch<TSolutionContext>>("LocalSearch", "The local search operator to use."));
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[14420] | 155 |
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| 156 | qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
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| 157 | RegisterEventHandlers();
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| 158 | }
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| 159 |
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| 160 | [StorableHook(HookType.AfterDeserialization)]
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| 161 | private void AfterDeserialization() {
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| 162 | RegisterEventHandlers();
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| 163 | }
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| 164 |
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| 165 | private void RegisterEventHandlers() {
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| 166 | MaximumPopulationSizeParameter.Value.ValueChanged += MaximumPopulationSizeOnChanged;
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| 167 | }
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| 168 |
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| 169 | private void MaximumPopulationSizeOnChanged(object sender, EventArgs eventArgs) {
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| 170 | if (ExecutionState == ExecutionState.Started || ExecutionState == ExecutionState.Paused)
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| 171 | throw new InvalidOperationException("Cannot change maximum population size before algorithm finishes.");
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| 172 | Prepare();
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| 173 | }
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| 174 |
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| 175 | protected override void OnProblemChanged() {
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| 176 | base.OnProblemChanged();
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| 177 | qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
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| 178 | qualityAnalyzer.MaximizationParameter.Hidden = true;
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| 179 | qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
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| 180 | qualityAnalyzer.QualityParameter.Depth = 1;
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| 181 | qualityAnalyzer.QualityParameter.Hidden = true;
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| 182 | qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
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| 183 | qualityAnalyzer.BestKnownQualityParameter.Hidden = true;
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| 184 |
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| 185 | var multiAnalyzer = Analyzer as MultiAnalyzer;
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| 186 | if (multiAnalyzer != null) {
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| 187 | multiAnalyzer.Operators.Clear();
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| 188 | if (Problem != null) {
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| 189 | foreach (var analyzer in Problem.Operators.OfType<IAnalyzer>()) {
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| 190 | foreach (var param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
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| 191 | param.Depth = 1;
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| 192 | multiAnalyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
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| 193 | }
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| 194 | }
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| 195 | multiAnalyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
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| 196 | }
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| 197 | }
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| 198 |
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| 199 | public override void Prepare() {
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| 200 | base.Prepare();
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| 201 | Results.Clear();
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| 202 | Context = null;
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| 203 | }
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| 204 |
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[14450] | 205 | protected virtual TPopulationContext CreateContext() {
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| 206 | return new TPopulationContext();
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[14420] | 207 | }
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| 208 |
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| 209 | protected sealed override void Run(CancellationToken token) {
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| 210 | if (Context == null) {
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| 211 | Context = CreateContext();
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| 212 | if (SetSeedRandomly) Seed = new System.Random().Next();
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| 213 | Context.Random.Reset(Seed);
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| 214 | Context.Scope.Variables.Add(new Variable("Results", Results));
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[14450] | 215 | Context.Problem = Problem;
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[14420] | 216 | }
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| 217 |
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[14477] | 218 | if (MaximumExecutionTime.HasValue)
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| 219 | CancellationTokenSource.CancelAfter(MaximumExecutionTime.Value);
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| 220 |
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[14420] | 221 | IExecutionContext context = null;
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| 222 | foreach (var item in Problem.ExecutionContextItems)
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| 223 | context = new Core.ExecutionContext(context, item, Context.Scope);
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| 224 | context = new Core.ExecutionContext(context, this, Context.Scope);
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| 225 | Context.Parent = context;
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| 226 |
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| 227 | if (!Context.Initialized) {
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| 228 | // We initialize the population with two local optima
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| 229 | while (Context.PopulationCount < 2) {
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| 230 | var child = Create(token);
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[14496] | 231 | Context.LocalSearchEvaluations += HillClimb(child, token);
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[14550] | 232 | Context.LocalOptimaLevel += child.Fitness;
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[14544] | 233 | Context.AddToPopulation(child);
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| 234 | Context.BestQuality = child.Fitness;
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| 235 | Analyze(token);
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[14420] | 236 | token.ThrowIfCancellationRequested();
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[14456] | 237 | if (Terminate()) return;
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[14420] | 238 | }
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[14496] | 239 | Context.LocalSearchEvaluations /= 2;
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[14550] | 240 | Context.LocalOptimaLevel /= 2;
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[14420] | 241 | Context.Initialized = true;
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| 242 | }
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| 243 |
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| 244 | while (!Terminate()) {
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| 245 | Iterate(token);
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| 246 | Analyze(token);
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| 247 | token.ThrowIfCancellationRequested();
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| 248 | }
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| 249 | }
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| 250 |
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| 251 | private void Iterate(CancellationToken token) {
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| 252 | var replaced = false;
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| 253 | ISingleObjectiveSolutionScope<TSolution> offspring = null;
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[14544] | 254 |
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| 255 | offspring = Breed(token);
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| 256 | if (offspring != null) {
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| 257 | if (Context.PopulationCount < MaximumPopulationSize)
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[14550] | 258 | AdaptiveClimb(offspring, Context.LocalSearchEvaluations, token);
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[14544] | 259 | var replNew = Replace(offspring, token);
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| 260 | if (replNew) {
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[14420] | 261 | replaced = true;
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| 262 | Context.ByBreeding++;
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| 263 | }
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| 264 | }
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| 265 |
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[14544] | 266 | offspring = Relink(token);
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| 267 | if (offspring != null) {
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| 268 | if (Context.PopulationCount < MaximumPopulationSize)
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[14550] | 269 | AdaptiveClimb(offspring, Context.LocalSearchEvaluations, token);
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[14544] | 270 | if (Replace(offspring, token)) {
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[14420] | 271 | replaced = true;
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| 272 | Context.ByRelinking++;
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| 273 | }
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| 274 | }
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| 275 |
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[14544] | 276 | offspring = Delink(token);
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| 277 | if (offspring != null) {
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| 278 | if (Context.PopulationCount < MaximumPopulationSize)
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[14550] | 279 | AdaptiveClimb(offspring, Context.LocalSearchEvaluations, token);
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[14544] | 280 | if (Replace(offspring, token)) {
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| 281 | replaced = true;
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| 282 | Context.ByDelinking++;
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| 283 | }
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[14420] | 284 | }
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| 285 |
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[14544] | 286 | offspring = Sample(token);
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| 287 | if (offspring != null) {
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| 288 | if (Context.PopulationCount < MaximumPopulationSize)
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[14550] | 289 | AdaptiveClimb(offspring, Context.LocalSearchEvaluations, token);
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[14544] | 290 | if (Replace(offspring, token)) {
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| 291 | replaced = true;
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| 292 | Context.BySampling++;
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| 293 | }
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| 294 | }
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| 295 |
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| 296 | if (!replaced && offspring != null) {
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| 297 | if (Context.HillclimbingSuited(offspring)) {
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[14550] | 298 | AdaptiveClimb(offspring, Context.LocalSearchEvaluations, token);
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[14544] | 299 | if (Replace(offspring, token)) {
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[14420] | 300 | Context.ByHillclimbing++;
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| 301 | replaced = true;
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| 302 | }
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| 303 | }
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| 304 | }
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[14544] | 305 |
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| 306 | if (!replaced) {
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| 307 | offspring = (ISingleObjectiveSolutionScope<TSolution>)Context.Population.SampleRandom(Context.Random).Clone();
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| 308 | var before = offspring.Fitness;
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| 309 | AdaptiveWalk(offspring, Context.LocalSearchEvaluations * 2, token);
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| 310 | Context.AdaptivewalkingStat.Add(Tuple.Create(before, offspring.Fitness));
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| 311 | if (Context.AdaptivewalkingStat.Count % 10 == 0) Context.RelearnAdaptiveWalkPerformanceModel();
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| 312 | if (Replace(offspring, token)) {
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| 313 | Context.ByAdaptivewalking++;
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| 314 | replaced = true;
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| 315 | }
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| 316 | }
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| 317 |
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[14420] | 318 | Context.Iterations++;
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| 319 | }
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| 320 |
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| 321 | protected void Analyze(CancellationToken token) {
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| 322 | IResult res;
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| 323 | if (!Results.TryGetValue("EvaluatedSolutions", out res))
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| 324 | Results.Add(new Result("EvaluatedSolutions", new IntValue(Context.EvaluatedSolutions)));
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| 325 | else ((IntValue)res.Value).Value = Context.EvaluatedSolutions;
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| 326 | if (!Results.TryGetValue("Iterations", out res))
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| 327 | Results.Add(new Result("Iterations", new IntValue(Context.Iterations)));
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| 328 | else ((IntValue)res.Value).Value = Context.Iterations;
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[14496] | 329 | if (!Results.TryGetValue("LocalSearch Evaluations", out res))
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| 330 | Results.Add(new Result("LocalSearch Evaluations", new IntValue(Context.LocalSearchEvaluations)));
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| 331 | else ((IntValue)res.Value).Value = Context.LocalSearchEvaluations;
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[14420] | 332 | if (!Results.TryGetValue("ByBreeding", out res))
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| 333 | Results.Add(new Result("ByBreeding", new IntValue(Context.ByBreeding)));
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| 334 | else ((IntValue)res.Value).Value = Context.ByBreeding;
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| 335 | if (!Results.TryGetValue("ByRelinking", out res))
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| 336 | Results.Add(new Result("ByRelinking", new IntValue(Context.ByRelinking)));
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| 337 | else ((IntValue)res.Value).Value = Context.ByRelinking;
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[14544] | 338 | if (!Results.TryGetValue("ByDelinking", out res))
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| 339 | Results.Add(new Result("ByDelinking", new IntValue(Context.ByDelinking)));
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| 340 | else ((IntValue)res.Value).Value = Context.ByDelinking;
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[14420] | 341 | if (!Results.TryGetValue("BySampling", out res))
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| 342 | Results.Add(new Result("BySampling", new IntValue(Context.BySampling)));
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| 343 | else ((IntValue)res.Value).Value = Context.BySampling;
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| 344 | if (!Results.TryGetValue("ByHillclimbing", out res))
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| 345 | Results.Add(new Result("ByHillclimbing", new IntValue(Context.ByHillclimbing)));
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| 346 | else ((IntValue)res.Value).Value = Context.ByHillclimbing;
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[14544] | 347 | if (!Results.TryGetValue("ByAdaptivewalking", out res))
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| 348 | Results.Add(new Result("ByAdaptivewalking", new IntValue(Context.ByAdaptivewalking)));
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| 349 | else ((IntValue)res.Value).Value = Context.ByAdaptivewalking;
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[14420] | 350 |
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[14544] | 351 | var sp = new ScatterPlot("Breeding Correlation", "");
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| 352 | sp.Rows.Add(new ScatterPlotDataRow("Parent1 vs Offspring", "", Context.BreedingStat.Select(x => new Point2D<double>(x.Item1, x.Item3))) { VisualProperties = { PointSize = 6 }});
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| 353 | sp.Rows.Add(new ScatterPlotDataRow("Parent2 vs Offspring", "", Context.BreedingStat.Select(x => new Point2D<double>(x.Item2, x.Item3))) { VisualProperties = { PointSize = 6 } });
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| 354 | if (!Results.TryGetValue("BreedingStat", out res)) {
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| 355 | Results.Add(new Result("BreedingStat", sp));
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[14420] | 356 | } else res.Value = sp;
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| 357 |
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[14544] | 358 | sp = new ScatterPlot("Relinking Correlation", "");
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| 359 | sp.Rows.Add(new ScatterPlotDataRow("A vs Relink", "", Context.RelinkingStat.Select(x => new Point2D<double>(x.Item1, x.Item3))) { VisualProperties = { PointSize = 6 } });
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| 360 | sp.Rows.Add(new ScatterPlotDataRow("B vs Relink", "", Context.RelinkingStat.Select(x => new Point2D<double>(x.Item2, x.Item3))) { VisualProperties = { PointSize = 6 } });
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| 361 | if (!Results.TryGetValue("RelinkingStat", out res)) {
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| 362 | Results.Add(new Result("RelinkingStat", sp));
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[14420] | 363 | } else res.Value = sp;
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| 364 |
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[14544] | 365 | sp = new ScatterPlot("Delinking Correlation", "");
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| 366 | sp.Rows.Add(new ScatterPlotDataRow("A vs Delink", "", Context.DelinkingStat.Select(x => new Point2D<double>(x.Item1, x.Item3))) { VisualProperties = { PointSize = 6 } });
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| 367 | sp.Rows.Add(new ScatterPlotDataRow("B vs Delink", "", Context.DelinkingStat.Select(x => new Point2D<double>(x.Item2, x.Item3))) { VisualProperties = { PointSize = 6 } });
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| 368 | if (!Results.TryGetValue("DelinkingStat", out res)) {
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| 369 | Results.Add(new Result("DelinkingStat", sp));
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| 370 | } else res.Value = sp;
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| 371 |
|
---|
| 372 | sp = new ScatterPlot("Sampling Correlation", "");
|
---|
| 373 | sp.Rows.Add(new ScatterPlotDataRow("AvgFitness vs Sample", "", Context.SamplingStat.Select(x => new Point2D<double>(x.Item1, x.Item2))) { VisualProperties = { PointSize = 6 } });
|
---|
| 374 | if (!Results.TryGetValue("SampleStat", out res)) {
|
---|
| 375 | Results.Add(new Result("SampleStat", sp));
|
---|
| 376 | } else res.Value = sp;
|
---|
| 377 |
|
---|
| 378 | sp = new ScatterPlot("Hillclimbing Correlation", "");
|
---|
| 379 | sp.Rows.Add(new ScatterPlotDataRow("Start vs End", "", Context.HillclimbingStat.Select(x => new Point2D<double>(x.Item1, x.Item2))) { VisualProperties = { PointSize = 6 } });
|
---|
[14420] | 380 | if (!Results.TryGetValue("HillclimbingStat", out res)) {
|
---|
| 381 | Results.Add(new Result("HillclimbingStat", sp));
|
---|
| 382 | } else res.Value = sp;
|
---|
| 383 |
|
---|
[14544] | 384 | sp = new ScatterPlot("Adaptivewalking Correlation", "");
|
---|
| 385 | sp.Rows.Add(new ScatterPlotDataRow("Start vs Best", "", Context.AdaptivewalkingStat.Select(x => new Point2D<double>(x.Item1, x.Item2))) { VisualProperties = { PointSize = 6 } });
|
---|
| 386 | if (!Results.TryGetValue("AdaptivewalkingStat", out res)) {
|
---|
| 387 | Results.Add(new Result("AdaptivewalkingStat", sp));
|
---|
[14420] | 388 | } else res.Value = sp;
|
---|
| 389 |
|
---|
[14544] | 390 | if (Context.BreedingPerformanceModel != null) {
|
---|
| 391 | var sol = Context.GetSolution(Context.BreedingPerformanceModel, Context.BreedingStat);
|
---|
| 392 | if (!Results.TryGetValue("Breeding Performance", out res)) {
|
---|
| 393 | Results.Add(new Result("Breeding Performance", sol));
|
---|
| 394 | } else res.Value = sol;
|
---|
| 395 | }
|
---|
| 396 | if (Context.RelinkingPerformanceModel != null) {
|
---|
| 397 | var sol = Context.GetSolution(Context.RelinkingPerformanceModel, Context.RelinkingStat);
|
---|
| 398 | if (!Results.TryGetValue("Relinking Performance", out res)) {
|
---|
| 399 | Results.Add(new Result("Relinking Performance", sol));
|
---|
| 400 | } else res.Value = sol;
|
---|
| 401 | }
|
---|
| 402 | if (Context.DelinkingPerformanceModel != null) {
|
---|
| 403 | var sol = Context.GetSolution(Context.DelinkingPerformanceModel, Context.DelinkingStat);
|
---|
| 404 | if (!Results.TryGetValue("Delinking Performance", out res)) {
|
---|
| 405 | Results.Add(new Result("Delinking Performance", sol));
|
---|
| 406 | } else res.Value = sol;
|
---|
| 407 | }
|
---|
| 408 | if (Context.SamplingPerformanceModel != null) {
|
---|
| 409 | var sol = Context.GetSolution(Context.SamplingPerformanceModel, Context.SamplingStat);
|
---|
| 410 | if (!Results.TryGetValue("Sampling Performance", out res)) {
|
---|
| 411 | Results.Add(new Result("Sampling Performance", sol));
|
---|
| 412 | } else res.Value = sol;
|
---|
| 413 | }
|
---|
| 414 | if (Context.HillclimbingPerformanceModel != null) {
|
---|
| 415 | var sol = Context.GetSolution(Context.HillclimbingPerformanceModel, Context.HillclimbingStat);
|
---|
| 416 | if (!Results.TryGetValue("Hillclimbing Performance", out res)) {
|
---|
| 417 | Results.Add(new Result("Hillclimbing Performance", sol));
|
---|
| 418 | } else res.Value = sol;
|
---|
| 419 | }
|
---|
| 420 | if (Context.AdaptiveWalkPerformanceModel != null) {
|
---|
| 421 | var sol = Context.GetSolution(Context.AdaptiveWalkPerformanceModel, Context.AdaptivewalkingStat);
|
---|
| 422 | if (!Results.TryGetValue("Adaptivewalk Performance", out res)) {
|
---|
| 423 | Results.Add(new Result("Adaptivewalk Performance", sol));
|
---|
| 424 | } else res.Value = sol;
|
---|
| 425 | }
|
---|
| 426 |
|
---|
[14420] | 427 | RunOperator(Analyzer, Context.Scope, token);
|
---|
| 428 | }
|
---|
| 429 |
|
---|
[14544] | 430 | protected bool Replace(ISingleObjectiveSolutionScope<TSolution> child, CancellationToken token) {
|
---|
[14453] | 431 | if (double.IsNaN(child.Fitness)) {
|
---|
| 432 | Evaluate(child, token);
|
---|
| 433 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 434 | }
|
---|
[14544] | 435 | if (Context.IsBetter(child.Fitness, Context.BestQuality)) {
|
---|
[14453] | 436 | Context.BestQuality = child.Fitness;
|
---|
| 437 | Context.BestSolution = (TSolution)child.Solution.Clone();
|
---|
| 438 | }
|
---|
[14420] | 439 |
|
---|
| 440 | var popSize = MaximumPopulationSize;
|
---|
| 441 | if (Context.Population.All(p => !Eq(p, child))) {
|
---|
| 442 |
|
---|
| 443 | if (Context.PopulationCount < popSize) {
|
---|
| 444 | Context.AddToPopulation(child);
|
---|
[14544] | 445 | return true;// Context.PopulationCount - 1;
|
---|
[14420] | 446 | }
|
---|
| 447 |
|
---|
| 448 | // The set of replacement candidates consists of all solutions at least as good as the new one
|
---|
| 449 | var candidates = Context.Population.Select((p, i) => new { Index = i, Individual = p })
|
---|
| 450 | .Where(x => x.Individual.Fitness == child.Fitness
|
---|
[14544] | 451 | || Context.IsBetter(child, x.Individual)).ToList();
|
---|
| 452 | if (candidates.Count == 0) return false;// -1;
|
---|
[14420] | 453 |
|
---|
| 454 | var repCand = -1;
|
---|
| 455 | var avgChildDist = 0.0;
|
---|
| 456 | var minChildDist = double.MaxValue;
|
---|
| 457 | var plateau = new List<int>();
|
---|
| 458 | var worstPlateau = -1;
|
---|
| 459 | var minAvgPlateauDist = double.MaxValue;
|
---|
| 460 | var minPlateauDist = double.MaxValue;
|
---|
| 461 | // If there are equally good solutions it is first tried to replace one of those
|
---|
| 462 | // The criteria for replacement is that the new solution has better average distance
|
---|
| 463 | // to all other solutions at this "plateau"
|
---|
| 464 | foreach (var c in candidates.Where(x => x.Individual.Fitness == child.Fitness)) {
|
---|
| 465 | var dist = Dist(c.Individual, child);
|
---|
| 466 | avgChildDist += dist;
|
---|
| 467 | if (dist < minChildDist) minChildDist = dist;
|
---|
| 468 | plateau.Add(c.Index);
|
---|
| 469 | }
|
---|
| 470 | if (plateau.Count > 2) {
|
---|
| 471 | avgChildDist /= plateau.Count;
|
---|
| 472 | foreach (var p in plateau) {
|
---|
| 473 | var avgDist = 0.0;
|
---|
| 474 | var minDist = double.MaxValue;
|
---|
| 475 | foreach (var q in plateau) {
|
---|
| 476 | if (p == q) continue;
|
---|
| 477 | var dist = Dist(Context.AtPopulation(p), Context.AtPopulation(q));
|
---|
| 478 | avgDist += dist;
|
---|
| 479 | if (dist < minDist) minDist = dist;
|
---|
| 480 | }
|
---|
| 481 |
|
---|
| 482 | var d = Dist(Context.AtPopulation(p), child);
|
---|
| 483 | avgDist += d;
|
---|
| 484 | avgDist /= plateau.Count;
|
---|
| 485 | if (d < minDist) minDist = d;
|
---|
| 486 |
|
---|
| 487 | if (minDist < minPlateauDist || (minDist == minPlateauDist && avgDist < avgChildDist)) {
|
---|
| 488 | minAvgPlateauDist = avgDist;
|
---|
| 489 | minPlateauDist = minDist;
|
---|
| 490 | worstPlateau = p;
|
---|
| 491 | }
|
---|
| 492 | }
|
---|
| 493 | if (minPlateauDist < minChildDist || (minPlateauDist == minChildDist && minAvgPlateauDist < avgChildDist))
|
---|
| 494 | repCand = worstPlateau;
|
---|
| 495 | }
|
---|
| 496 |
|
---|
| 497 | if (repCand < 0) {
|
---|
| 498 | // If no solution at the same plateau were identified for replacement
|
---|
| 499 | // a worse solution with smallest distance is chosen
|
---|
| 500 | var minDist = double.MaxValue;
|
---|
[14544] | 501 | foreach (var c in candidates.Where(x => Context.IsBetter(child, x.Individual))) {
|
---|
[14420] | 502 | var d = Dist(c.Individual, child);
|
---|
| 503 | if (d < minDist) {
|
---|
| 504 | minDist = d;
|
---|
| 505 | repCand = c.Index;
|
---|
| 506 | }
|
---|
| 507 | }
|
---|
| 508 | }
|
---|
| 509 |
|
---|
| 510 | // If no replacement was identified, this can only mean that there are
|
---|
| 511 | // no worse solutions and those on the same plateau are all better
|
---|
| 512 | // stretched out than the new one
|
---|
[14544] | 513 | if (repCand < 0) return false;// -1;
|
---|
[14420] | 514 |
|
---|
| 515 | Context.ReplaceAtPopulation(repCand, child);
|
---|
[14544] | 516 | return true;// repCand;
|
---|
[14420] | 517 | }
|
---|
[14544] | 518 | return false;// -1;
|
---|
[14420] | 519 | }
|
---|
[14550] | 520 |
|
---|
| 521 | protected bool Eq(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b) {
|
---|
| 522 | return Eq(a.Solution, b.Solution);
|
---|
| 523 | }
|
---|
| 524 | protected abstract bool Eq(TSolution a, TSolution b);
|
---|
[14420] | 525 | protected abstract double Dist(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b);
|
---|
| 526 | protected abstract ISingleObjectiveSolutionScope<TSolution> ToScope(TSolution code, double fitness = double.NaN);
|
---|
[14450] | 527 | protected abstract ISolutionSubspace<TSolution> CalculateSubspace(IEnumerable<TSolution> solutions, bool inverse = false);
|
---|
[14420] | 528 | protected virtual void Evaluate(ISingleObjectiveSolutionScope<TSolution> scope, CancellationToken token) {
|
---|
| 529 | var prob = Problem as ISingleObjectiveProblemDefinition;
|
---|
| 530 | if (prob != null) {
|
---|
| 531 | var ind = new SingleEncodingIndividual(prob.Encoding, scope);
|
---|
| 532 | scope.Fitness = prob.Evaluate(ind, Context.Random);
|
---|
| 533 | } else RunOperator(Problem.Evaluator, scope, token);
|
---|
| 534 | }
|
---|
| 535 |
|
---|
| 536 | #region Create
|
---|
[14450] | 537 | protected virtual ISingleObjectiveSolutionScope<TSolution> Create(CancellationToken token) {
|
---|
| 538 | var child = ToScope(null);
|
---|
| 539 | RunOperator(Problem.SolutionCreator, child, token);
|
---|
| 540 | return child;
|
---|
| 541 | }
|
---|
[14420] | 542 | #endregion
|
---|
| 543 |
|
---|
| 544 | #region Improve
|
---|
[14450] | 545 | protected virtual int HillClimb(ISingleObjectiveSolutionScope<TSolution> scope, CancellationToken token, ISolutionSubspace<TSolution> subspace = null) {
|
---|
[14453] | 546 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 547 | Evaluate(scope, token);
|
---|
| 548 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 549 | }
|
---|
[14420] | 550 | var before = scope.Fitness;
|
---|
| 551 | var lscontext = Context.CreateSingleSolutionContext(scope);
|
---|
| 552 | LocalSearchParameter.Value.Optimize(lscontext);
|
---|
| 553 | var after = scope.Fitness;
|
---|
| 554 | Context.HillclimbingStat.Add(Tuple.Create(before, after));
|
---|
[14544] | 555 | if (Context.HillclimbingStat.Count % 10 == 0) Context.RelearnHillclimbingPerformanceModel();
|
---|
[14453] | 556 | Context.IncrementEvaluatedSolutions(lscontext.EvaluatedSolutions);
|
---|
[14456] | 557 | return lscontext.EvaluatedSolutions;
|
---|
[14420] | 558 | }
|
---|
| 559 |
|
---|
[14544] | 560 | protected virtual void AdaptiveClimb(ISingleObjectiveSolutionScope<TSolution> scope, int maxEvals, CancellationToken token, ISolutionSubspace<TSolution> subspace = null) {
|
---|
[14453] | 561 | if (double.IsNaN(scope.Fitness)) {
|
---|
| 562 | Evaluate(scope, token);
|
---|
| 563 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 564 | }
|
---|
[14420] | 565 | var before = scope.Fitness;
|
---|
| 566 | var newScope = (ISingleObjectiveSolutionScope<TSolution>)scope.Clone();
|
---|
[14544] | 567 | AdaptiveWalk(newScope, maxEvals, token, subspace);
|
---|
| 568 | Context.AdaptivewalkingStat.Add(Tuple.Create(before, newScope.Fitness));
|
---|
| 569 | if (Context.AdaptivewalkingStat.Count % 10 == 0) Context.RelearnAdaptiveWalkPerformanceModel();
|
---|
| 570 | if (Context.IsBetter(newScope, scope))
|
---|
[14420] | 571 | scope.Adopt(newScope);
|
---|
| 572 | }
|
---|
[14544] | 573 | protected abstract void AdaptiveWalk(ISingleObjectiveSolutionScope<TSolution> scope, int maxEvals, CancellationToken token, ISolutionSubspace<TSolution> subspace = null);
|
---|
| 574 |
|
---|
[14420] | 575 | #endregion
|
---|
[14544] | 576 |
|
---|
[14420] | 577 | #region Breed
|
---|
[14544] | 578 | protected virtual ISingleObjectiveSolutionScope<TSolution> Breed(CancellationToken token) {
|
---|
[14420] | 579 | var i1 = Context.Random.Next(Context.PopulationCount);
|
---|
| 580 | var i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 581 | while (i1 == i2) i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 582 |
|
---|
| 583 | var p1 = Context.AtPopulation(i1);
|
---|
| 584 | var p2 = Context.AtPopulation(i2);
|
---|
| 585 |
|
---|
[14453] | 586 | if (double.IsNaN(p1.Fitness)) {
|
---|
| 587 | Evaluate(p1, token);
|
---|
| 588 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 589 | }
|
---|
| 590 | if (double.IsNaN(p2.Fitness)) {
|
---|
| 591 | Evaluate(p2, token);
|
---|
| 592 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 593 | }
|
---|
[14420] | 594 |
|
---|
[14544] | 595 | if (Context.BreedingSuited(p1, p2)) {
|
---|
| 596 | var offspring = Breed(p1, p2, token);
|
---|
[14420] | 597 |
|
---|
[14544] | 598 | if (double.IsNaN(offspring.Fitness)) {
|
---|
| 599 | Evaluate(offspring, token);
|
---|
| 600 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 601 | }
|
---|
| 602 |
|
---|
| 603 | // new best solutions are improved using hill climbing in full solution space
|
---|
| 604 | if (Context.Population.All(p => Context.IsBetter(offspring, p)))
|
---|
| 605 | HillClimb(offspring, token);
|
---|
[14550] | 606 | else if (!Eq(offspring, p1) && !Eq(offspring, p2) && Context.HillclimbingSuited(offspring.Fitness))
|
---|
| 607 | HillClimb(offspring, token, CalculateSubspace(new[] { p1.Solution, p2.Solution }, inverse: false));
|
---|
[14544] | 608 |
|
---|
| 609 | Context.AddBreedingResult(p1, p2, offspring);
|
---|
| 610 | if (Context.BreedingStat.Count % 10 == 0) Context.RelearnBreedingPerformanceModel();
|
---|
| 611 | return offspring;
|
---|
[14420] | 612 | }
|
---|
[14544] | 613 | return null;
|
---|
[14420] | 614 | }
|
---|
| 615 |
|
---|
[14544] | 616 | protected abstract ISingleObjectiveSolutionScope<TSolution> Breed(ISingleObjectiveSolutionScope<TSolution> p1, ISingleObjectiveSolutionScope<TSolution> p2, CancellationToken token);
|
---|
[14420] | 617 | #endregion
|
---|
| 618 |
|
---|
[14544] | 619 | #region Relink/Delink
|
---|
| 620 | protected virtual ISingleObjectiveSolutionScope<TSolution> Relink(CancellationToken token) {
|
---|
[14420] | 621 | var i1 = Context.Random.Next(Context.PopulationCount);
|
---|
| 622 | var i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 623 | while (i1 == i2) i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 624 |
|
---|
| 625 | var p1 = Context.AtPopulation(i1);
|
---|
| 626 | var p2 = Context.AtPopulation(i2);
|
---|
| 627 |
|
---|
[14550] | 628 | if (!Context.RelinkSuited(p1, p2)) return null;
|
---|
| 629 |
|
---|
| 630 | var link = PerformRelinking(p1, p2, token, delink: false);
|
---|
| 631 | if (double.IsNaN(link.Fitness)) {
|
---|
| 632 | Evaluate(link, token);
|
---|
| 633 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 634 | }
|
---|
| 635 | // new best solutions are improved using hill climbing in full solution space
|
---|
| 636 | if (Context.Population.All(p => Context.IsBetter(link, p)))
|
---|
| 637 | HillClimb(link, token);
|
---|
| 638 | else if (!Eq(link, p1) && !Eq(link, p2) && Context.HillclimbingSuited(link.Fitness))
|
---|
| 639 | HillClimb(link, token, CalculateSubspace(new[] { p1.Solution, p2.Solution }, inverse: true));
|
---|
| 640 |
|
---|
| 641 | return link;
|
---|
[14420] | 642 | }
|
---|
| 643 |
|
---|
[14544] | 644 | protected virtual ISingleObjectiveSolutionScope<TSolution> Delink(CancellationToken token) {
|
---|
| 645 | var i1 = Context.Random.Next(Context.PopulationCount);
|
---|
| 646 | var i2 = Context.Random.Next(Context.PopulationCount);
|
---|
| 647 | while (i1 == i2) i2 = Context.Random.Next(Context.PopulationCount);
|
---|
[14420] | 648 |
|
---|
[14544] | 649 | var p1 = Context.AtPopulation(i1);
|
---|
| 650 | var p2 = Context.AtPopulation(i2);
|
---|
[14550] | 651 |
|
---|
| 652 | if (!Context.DelinkSuited(p1, p2)) return null;
|
---|
[14544] | 653 |
|
---|
[14550] | 654 | var link = PerformRelinking(p1, p2, token, delink: true);
|
---|
| 655 | if (double.IsNaN(link.Fitness)) {
|
---|
| 656 | Evaluate(link, token);
|
---|
| 657 | Context.IncrementEvaluatedSolutions(1);
|
---|
| 658 | }
|
---|
| 659 | // new best solutions are improved using hill climbing in full solution space
|
---|
| 660 | if (Context.Population.All(p => Context.IsBetter(link, p)))
|
---|
| 661 | HillClimb(link, token);
|
---|
| 662 | /*else if (!Eq(link, p1) && !Eq(link, p2) && Context.HillclimbingSuited(link.Fitness))
|
---|
| 663 | HillClimb(link, token, CalculateSubspace(new[] { p1.Solution, p2.Solution }, inverse: false));*/
|
---|
| 664 |
|
---|
| 665 | return link;
|
---|
[14420] | 666 | }
|
---|
| 667 |
|
---|
[14544] | 668 | protected virtual ISingleObjectiveSolutionScope<TSolution> PerformRelinking(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, CancellationToken token, bool delink = false) {
|
---|
| 669 | var relink = Link(a, b, token, delink);
|
---|
[14420] | 670 |
|
---|
[14544] | 671 | if (double.IsNaN(relink.Fitness)) {
|
---|
| 672 | Evaluate(relink, token);
|
---|
| 673 | Context.IncrementEvaluatedSolutions(1);
|
---|
[14420] | 674 | }
|
---|
| 675 |
|
---|
[14544] | 676 | // new best solutions are improved using hill climbing
|
---|
| 677 | if (Context.Population.All(p => Context.IsBetter(relink, p)))
|
---|
| 678 | HillClimb(relink, token);
|
---|
[14420] | 679 |
|
---|
[14544] | 680 | if (delink) {
|
---|
| 681 | Context.AddDelinkingResult(a, b, relink);
|
---|
| 682 | if (Context.DelinkingStat.Count % 10 == 0) Context.RelearnDelinkingPerformanceModel();
|
---|
| 683 | } else {
|
---|
| 684 | Context.AddRelinkingResult(a, b, relink);
|
---|
| 685 | if (context.RelinkingStat.Count % 10 == 0) Context.RelearnRelinkingPerformanceModel();
|
---|
[14453] | 686 | }
|
---|
[14544] | 687 | return relink;
|
---|
[14420] | 688 | }
|
---|
| 689 |
|
---|
[14544] | 690 | protected abstract ISingleObjectiveSolutionScope<TSolution> Link(ISingleObjectiveSolutionScope<TSolution> a, ISingleObjectiveSolutionScope<TSolution> b, CancellationToken token, bool delink = false);
|
---|
| 691 | #endregion
|
---|
[14420] | 692 |
|
---|
[14544] | 693 | #region Sample
|
---|
| 694 | protected virtual ISingleObjectiveSolutionScope<TSolution> Sample(CancellationToken token) {
|
---|
[14550] | 695 | if (Context.PopulationCount == MaximumPopulationSize) {
|
---|
[14544] | 696 | SolutionModelTrainerParameter.Value.TrainModel(Context);
|
---|
| 697 | ISingleObjectiveSolutionScope<TSolution> bestSample = null;
|
---|
| 698 | var tries = 1;
|
---|
[14550] | 699 | for (; tries < 100; tries++) {
|
---|
[14544] | 700 | var sample = ToScope(Context.Model.Sample());
|
---|
| 701 | Evaluate(sample, token);
|
---|
| 702 | if (bestSample == null || Context.IsBetter(sample, bestSample)) {
|
---|
| 703 | bestSample = sample;
|
---|
[14550] | 704 | if (Context.Population.Any(x => !Context.IsBetter(x, bestSample))) break;
|
---|
[14544] | 705 | }
|
---|
[14550] | 706 | if (!Context.SamplingSuited()) break;
|
---|
[14420] | 707 | }
|
---|
[14544] | 708 | Context.IncrementEvaluatedSolutions(tries);
|
---|
| 709 | Context.AddSamplingResult(bestSample);
|
---|
| 710 | if (Context.SamplingStat.Count % 10 == 0) Context.RelearnSamplingPerformanceModel();
|
---|
| 711 | return bestSample;
|
---|
[14420] | 712 | }
|
---|
[14544] | 713 | return null;
|
---|
[14420] | 714 | }
|
---|
[14544] | 715 | #endregion
|
---|
[14420] | 716 |
|
---|
| 717 | protected virtual bool Terminate() {
|
---|
| 718 | return MaximumEvaluations.HasValue && Context.EvaluatedSolutions >= MaximumEvaluations.Value
|
---|
| 719 | || MaximumExecutionTime.HasValue && ExecutionTime >= MaximumExecutionTime.Value
|
---|
[14450] | 720 | || TargetQuality.HasValue && (Problem.Maximization && Context.BestQuality >= TargetQuality.Value
|
---|
| 721 | || !Problem.Maximization && Context.BestQuality <= TargetQuality.Value);
|
---|
[14420] | 722 | }
|
---|
| 723 |
|
---|
| 724 | public event PropertyChangedEventHandler PropertyChanged;
|
---|
| 725 | protected void OnPropertyChanged(string property) {
|
---|
| 726 | var handler = PropertyChanged;
|
---|
| 727 | if (handler != null) handler(this, new PropertyChangedEventArgs(property));
|
---|
| 728 | }
|
---|
| 729 |
|
---|
| 730 | #region Engine Helper
|
---|
| 731 | protected void RunOperator(IOperator op, IScope scope, CancellationToken cancellationToken) {
|
---|
| 732 | var stack = new Stack<IOperation>();
|
---|
| 733 | stack.Push(Context.CreateChildOperation(op, scope));
|
---|
| 734 |
|
---|
| 735 | while (stack.Count > 0) {
|
---|
| 736 | cancellationToken.ThrowIfCancellationRequested();
|
---|
| 737 |
|
---|
| 738 | var next = stack.Pop();
|
---|
| 739 | if (next is OperationCollection) {
|
---|
| 740 | var coll = (OperationCollection)next;
|
---|
| 741 | for (int i = coll.Count - 1; i >= 0; i--)
|
---|
| 742 | if (coll[i] != null) stack.Push(coll[i]);
|
---|
| 743 | } else if (next is IAtomicOperation) {
|
---|
| 744 | var operation = (IAtomicOperation)next;
|
---|
| 745 | try {
|
---|
| 746 | next = operation.Operator.Execute((IExecutionContext)operation, cancellationToken);
|
---|
| 747 | } catch (Exception ex) {
|
---|
| 748 | stack.Push(operation);
|
---|
| 749 | if (ex is OperationCanceledException) throw ex;
|
---|
| 750 | else throw new OperatorExecutionException(operation.Operator, ex);
|
---|
| 751 | }
|
---|
| 752 | if (next != null) stack.Push(next);
|
---|
| 753 | }
|
---|
| 754 | }
|
---|
| 755 | }
|
---|
| 756 | #endregion
|
---|
| 757 | }
|
---|
| 758 | }
|
---|