1 | using HeuristicLab.Common;
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2 | using HeuristicLab.Core;
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3 | using HeuristicLab.Data;
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4 | using HeuristicLab.Operators;
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5 | using HeuristicLab.Optimization;
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6 | using HeuristicLab.Parameters;
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7 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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8 |
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9 | namespace HeuristicLab.Problems.MetaOptimization {
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10 | [Item("PMOEvaluator", "An operator which represents the main loop of a genetic algorithm.")]
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11 | [StorableClass]
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12 | public class PMOEvaluator : AlgorithmOperator, IParameterConfigurationEvaluator {
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13 |
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14 | #region Parameter properties
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15 | public ILookupParameter<IRandom> RandomParameter {
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16 | get { return (LookupParameter<IRandom>)Parameters["Random"]; }
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17 | }
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18 | public ILookupParameter<DoubleValue> QualityParameter {
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19 | get { return (ILookupParameter<DoubleValue>)Parameters["Quality"]; }
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20 | }
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21 | public ILookupParameter<TypeValue> AlgorithmTypeParameter {
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22 | get { return (ILookupParameter<TypeValue>)Parameters[MetaOptimizationProblem.AlgorithmTypeParameterName]; }
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23 | }
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24 | public ILookupParameter<IItemList<IProblem>> ProblemsParameter {
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25 | get { return (ILookupParameter<IItemList<IProblem>>)Parameters[MetaOptimizationProblem.ProblemsParameterName]; }
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26 | }
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27 | public ILookupParameter<ParameterConfigurationTree> ParameterConfigurationParameter {
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28 | get { return (ILookupParameter<ParameterConfigurationTree>)Parameters["ParameterConfigurationTree"]; }
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29 | }
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30 | public LookupParameter<IntValue> RepetitionsParameter {
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31 | get { return (LookupParameter<IntValue>)Parameters[MetaOptimizationProblem.RepetitionsParameterName]; }
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32 | }
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33 | public LookupParameter<IntValue> GenerationsParameter {
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34 | get { return (LookupParameter<IntValue>)Parameters["Generations"]; }
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35 | }
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36 | public LookupParameter<ResultCollection> ResultsParameter {
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37 | get { return (LookupParameter<ResultCollection>)Parameters["Results"]; }
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38 | }
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39 | private ScopeParameter CurrentScopeParameter {
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40 | get { return (ScopeParameter)Parameters["CurrentScope"]; }
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41 | }
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42 | public IScope CurrentScope {
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43 | get { return CurrentScopeParameter.ActualValue; }
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44 | }
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45 | #endregion
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46 |
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47 | [StorableConstructor]
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48 | protected PMOEvaluator(bool deserializing) : base(deserializing) { }
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49 | public PMOEvaluator() {
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50 | Initialize();
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51 | }
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52 | protected PMOEvaluator(PMOEvaluator original, Cloner cloner) : base(original, cloner) { }
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53 | public override IDeepCloneable Clone(Cloner cloner) {
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54 | return new PMOEvaluator(this, cloner);
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55 | }
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56 |
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57 | private void Initialize() {
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58 | #region Create parameters
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59 | Parameters.Add(new LookupParameter<IRandom>("Random", "The pseudo random number generator which should be used to initialize the new random permutation."));
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60 | Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The evaluated quality of the ParameterVector."));
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61 | Parameters.Add(new LookupParameter<TypeValue>(MetaOptimizationProblem.AlgorithmTypeParameterName, ""));
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62 | Parameters.Add(new LookupParameter<IItemList<IProblem>>(MetaOptimizationProblem.ProblemsParameterName, ""));
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63 | Parameters.Add(new LookupParameter<ParameterConfigurationTree>("ParameterConfigurationTree", ""));
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64 | Parameters.Add(new LookupParameter<IntValue>(MetaOptimizationProblem.RepetitionsParameterName, "Number of evaluations on one problem."));
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65 | Parameters.Add(new LookupParameter<IntValue>("Generations", ""));
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66 | Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied."));
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67 | #endregion
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68 |
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69 | var algorithmSubScopesCreator = new AlgorithmSubScopesCreator();
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70 | var uniformSubScopesProcessor = new UniformSubScopesProcessor();
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71 | var algorithmEvaluator = new AlgorithmEvaluator();
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72 | var algorithmRunsAnalyzer = new AlgorithmRunsAnalyzer();
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73 |
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74 | this.OperatorGraph.InitialOperator = algorithmSubScopesCreator;
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75 | algorithmSubScopesCreator.Successor = uniformSubScopesProcessor;
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76 | uniformSubScopesProcessor.Operator = algorithmEvaluator;
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77 | uniformSubScopesProcessor.Successor = algorithmRunsAnalyzer;
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78 | algorithmRunsAnalyzer.Successor = null;
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79 |
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80 | }
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81 |
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82 | }
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83 | }
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