[2830] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2010 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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[2891] | 22 | using HeuristicLab.Analysis;
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[3376] | 23 | using HeuristicLab.Common;
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[2830] | 24 | using HeuristicLab.Core;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Operators;
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[3021] | 27 | using HeuristicLab.Optimization.Operators;
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[2830] | 28 | using HeuristicLab.Parameters;
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[3000] | 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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[2830] | 30 | using HeuristicLab.Selection;
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| 31 |
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[3196] | 32 | namespace HeuristicLab.Algorithms.GeneticAlgorithm {
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[2830] | 33 | /// <summary>
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[3198] | 34 | /// An operator which represents the main loop of a genetic algorithm.
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[2830] | 35 | /// </summary>
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[3198] | 36 | [Item("GeneticAlgorithmMainLoop", "An operator which represents the main loop of a genetic algorithm.")]
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[3017] | 37 | [StorableClass]
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[3198] | 38 | public sealed class GeneticAlgorithmMainLoop : AlgorithmOperator {
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[2830] | 39 | #region Parameter properties
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| 40 | public ValueLookupParameter<IRandom> RandomParameter {
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| 41 | get { return (ValueLookupParameter<IRandom>)Parameters["Random"]; }
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| 42 | }
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[3048] | 43 | public ValueLookupParameter<BoolValue> MaximizationParameter {
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| 44 | get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
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[2830] | 45 | }
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[3659] | 46 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 47 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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[2830] | 48 | }
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[2882] | 49 | public ValueLookupParameter<IOperator> SelectorParameter {
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| 50 | get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
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[2830] | 51 | }
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[2882] | 52 | public ValueLookupParameter<IOperator> CrossoverParameter {
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| 53 | get { return (ValueLookupParameter<IOperator>)Parameters["Crossover"]; }
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| 54 | }
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[3095] | 55 | public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
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| 56 | get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
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[2830] | 57 | }
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[2882] | 58 | public ValueLookupParameter<IOperator> MutatorParameter {
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| 59 | get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
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[2830] | 60 | }
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[2882] | 61 | public ValueLookupParameter<IOperator> EvaluatorParameter {
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| 62 | get { return (ValueLookupParameter<IOperator>)Parameters["Evaluator"]; }
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[2830] | 63 | }
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[3048] | 64 | public ValueLookupParameter<IntValue> ElitesParameter {
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| 65 | get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
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[2830] | 66 | }
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[3048] | 67 | public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
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| 68 | get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
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[2830] | 69 | }
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[2882] | 70 | public ValueLookupParameter<VariableCollection> ResultsParameter {
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| 71 | get { return (ValueLookupParameter<VariableCollection>)Parameters["Results"]; }
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| 72 | }
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[3616] | 73 | public ValueLookupParameter<IOperator> AnalyzerParameter {
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| 74 | get { return (ValueLookupParameter<IOperator>)Parameters["Analyzer"]; }
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[3107] | 75 | }
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[2830] | 76 | private ScopeParameter CurrentScopeParameter {
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| 77 | get { return (ScopeParameter)Parameters["CurrentScope"]; }
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| 78 | }
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| 79 |
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| 80 | public IScope CurrentScope {
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| 81 | get { return CurrentScopeParameter.ActualValue; }
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| 82 | }
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| 83 | #endregion
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| 84 |
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[3080] | 85 | [StorableConstructor]
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[3198] | 86 | private GeneticAlgorithmMainLoop(bool deserializing) : base() { }
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| 87 | public GeneticAlgorithmMainLoop()
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[2830] | 88 | : base() {
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[3080] | 89 | Initialize();
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| 90 | }
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| 91 |
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| 92 | private void Initialize() {
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[2830] | 93 | #region Create parameters
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| 94 | Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
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[3048] | 95 | Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
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[3659] | 96 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
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[2882] | 97 | Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
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| 98 | Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
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[3095] | 99 | Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
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[2882] | 100 | Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
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| 101 | Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions."));
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[3048] | 102 | Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
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| 103 | Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
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[2882] | 104 | Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
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[3616] | 105 | Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
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[3198] | 106 | 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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[2830] | 107 | #endregion
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| 108 |
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[3080] | 109 | #region Create operators
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[2908] | 110 | VariableCreator variableCreator = new VariableCreator();
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[3616] | 111 | ResultsCollector resultsCollector1 = new ResultsCollector();
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| 112 | Placeholder analyzer1 = new Placeholder();
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[2882] | 113 | Placeholder selector = new Placeholder();
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[3193] | 114 | SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
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[2830] | 115 | ChildrenCreator childrenCreator = new ChildrenCreator();
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[3193] | 116 | UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
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[2830] | 117 | Placeholder crossover = new Placeholder();
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| 118 | StochasticBranch stochasticBranch = new StochasticBranch();
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| 119 | Placeholder mutator = new Placeholder();
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| 120 | Placeholder evaluator = new Placeholder();
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| 121 | SubScopesRemover subScopesRemover = new SubScopesRemover();
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[3193] | 122 | SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
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[3096] | 123 | BestSelector bestSelector = new BestSelector();
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[2830] | 124 | RightReducer rightReducer = new RightReducer();
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| 125 | MergingReducer mergingReducer = new MergingReducer();
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| 126 | IntCounter intCounter = new IntCounter();
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| 127 | Comparator comparator = new Comparator();
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[3616] | 128 | ResultsCollector resultsCollector2 = new ResultsCollector();
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| 129 | Placeholder analyzer2 = new Placeholder();
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[2830] | 130 | ConditionalBranch conditionalBranch = new ConditionalBranch();
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| 131 |
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[3750] | 132 | variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class GeneticAlgorithm expects this to be called Generations
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[2908] | 133 |
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[3616] | 134 | resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
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| 135 | resultsCollector1.ResultsParameter.ActualName = "Results";
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[3080] | 136 |
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[3616] | 137 | analyzer1.Name = "Analyzer";
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| 138 | analyzer1.OperatorParameter.ActualName = "Analyzer";
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[3095] | 139 |
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[2882] | 140 | selector.Name = "Selector";
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| 141 | selector.OperatorParameter.ActualName = "Selector";
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[2830] | 142 |
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[3048] | 143 | childrenCreator.ParentsPerChild = new IntValue(2);
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[2830] | 144 |
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[2882] | 145 | crossover.Name = "Crossover";
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| 146 | crossover.OperatorParameter.ActualName = "Crossover";
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[2830] | 147 |
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| 148 | stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability";
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| 149 | stochasticBranch.RandomParameter.ActualName = "Random";
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| 150 |
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[2882] | 151 | mutator.Name = "Mutator";
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| 152 | mutator.OperatorParameter.ActualName = "Mutator";
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[2830] | 153 |
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[2882] | 154 | evaluator.Name = "Evaluator";
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| 155 | evaluator.OperatorParameter.ActualName = "Evaluator";
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[2830] | 156 |
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| 157 | subScopesRemover.RemoveAllSubScopes = true;
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| 158 |
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[3096] | 159 | bestSelector.CopySelected = new BoolValue(false);
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| 160 | bestSelector.MaximizationParameter.ActualName = "Maximization";
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| 161 | bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites";
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| 162 | bestSelector.QualityParameter.ActualName = "Quality";
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[2830] | 163 |
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[3048] | 164 | intCounter.Increment = new IntValue(1);
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[2830] | 165 | intCounter.ValueParameter.ActualName = "Generations";
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| 166 |
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[3048] | 167 | comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
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[2830] | 168 | comparator.LeftSideParameter.ActualName = "Generations";
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| 169 | comparator.ResultParameter.ActualName = "Terminate";
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| 170 | comparator.RightSideParameter.ActualName = "MaximumGenerations";
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| 171 |
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[3616] | 172 | resultsCollector2.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
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| 173 | resultsCollector2.ResultsParameter.ActualName = "Results";
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[2891] | 174 |
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[3616] | 175 | analyzer2.Name = "Analyzer";
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| 176 | analyzer2.OperatorParameter.ActualName = "Analyzer";
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[3095] | 177 |
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[2830] | 178 | conditionalBranch.ConditionParameter.ActualName = "Terminate";
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[3080] | 179 | #endregion
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| 180 |
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| 181 | #region Create operator graph
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| 182 | OperatorGraph.InitialOperator = variableCreator;
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[3616] | 183 | variableCreator.Successor = resultsCollector1;
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| 184 | resultsCollector1.Successor = analyzer1;
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| 185 | analyzer1.Successor = selector;
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[3193] | 186 | selector.Successor = subScopesProcessor1;
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| 187 | subScopesProcessor1.Operators.Add(new EmptyOperator());
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| 188 | subScopesProcessor1.Operators.Add(childrenCreator);
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| 189 | subScopesProcessor1.Successor = subScopesProcessor2;
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| 190 | childrenCreator.Successor = uniformSubScopesProcessor;
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| 191 | uniformSubScopesProcessor.Operator = crossover;
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| 192 | uniformSubScopesProcessor.Successor = null;
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[3080] | 193 | crossover.Successor = stochasticBranch;
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| 194 | stochasticBranch.FirstBranch = mutator;
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| 195 | stochasticBranch.SecondBranch = null;
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| 196 | stochasticBranch.Successor = evaluator;
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| 197 | mutator.Successor = null;
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| 198 | evaluator.Successor = subScopesRemover;
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| 199 | subScopesRemover.Successor = null;
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[3193] | 200 | subScopesProcessor2.Operators.Add(bestSelector);
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| 201 | subScopesProcessor2.Operators.Add(new EmptyOperator());
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| 202 | subScopesProcessor2.Successor = mergingReducer;
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[3096] | 203 | bestSelector.Successor = rightReducer;
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[3080] | 204 | rightReducer.Successor = null;
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| 205 | mergingReducer.Successor = intCounter;
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| 206 | intCounter.Successor = comparator;
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[3616] | 207 | comparator.Successor = resultsCollector2;
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| 208 | resultsCollector2.Successor = analyzer2;
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| 209 | analyzer2.Successor = conditionalBranch;
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[3096] | 210 | conditionalBranch.FalseBranch = selector;
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[2830] | 211 | conditionalBranch.TrueBranch = null;
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| 212 | conditionalBranch.Successor = null;
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| 213 | #endregion
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| 214 | }
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[3715] | 215 |
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| 216 | public override IOperation Apply() {
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| 217 | if (CrossoverParameter.ActualValue == null)
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| 218 | return null;
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| 219 | return base.Apply();
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| 220 | }
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[2830] | 221 | }
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| 222 | }
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