| 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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| 22 | using HeuristicLab.Common;
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| 23 | using HeuristicLab.Core;
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| 24 | using HeuristicLab.Data;
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| 25 | using HeuristicLab.Operators;
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| 26 | using HeuristicLab.Optimization.Operators;
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| 27 | using HeuristicLab.Parameters;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Selection;
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| 30 |
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| 31 | namespace HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm {
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| 32 | /// <summary>
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| 33 | /// An operator which represents the main loop of an offspring selection genetic algorithm.
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| 34 | /// </summary>
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| 35 | [Item("OffspringSelectionGeneticAlgorithmMainOperator", "An operator that represents the core of an offspring selection genetic algorithm.")]
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| 36 | [StorableClass]
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| 37 | public sealed class OffspringSelectionGeneticAlgorithmMainOperator : AlgorithmOperator {
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| 38 | #region Parameter properties
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| 39 | public ValueLookupParameter<IRandom> RandomParameter {
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| 40 | get { return (ValueLookupParameter<IRandom>)Parameters["Random"]; }
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| 41 | }
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| 42 | public ValueLookupParameter<BoolValue> MaximizationParameter {
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| 43 | get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
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| 44 | }
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| 45 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 46 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 47 | }
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| 48 | public ValueLookupParameter<IOperator> SelectorParameter {
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| 49 | get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
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| 50 | }
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| 51 | public ValueLookupParameter<IOperator> CrossoverParameter {
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| 52 | get { return (ValueLookupParameter<IOperator>)Parameters["Crossover"]; }
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| 53 | }
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| 54 | public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
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| 55 | get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
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| 56 | }
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| 57 | public ValueLookupParameter<IOperator> MutatorParameter {
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| 58 | get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
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| 59 | }
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| 60 | public ValueLookupParameter<IOperator> EvaluatorParameter {
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| 61 | get { return (ValueLookupParameter<IOperator>)Parameters["Evaluator"]; }
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| 62 | }
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| 63 | public LookupParameter<IntValue> EvaluatedSolutionsParameter {
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| 64 | get { return (LookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
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| 65 | }
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| 66 | public ValueLookupParameter<IntValue> ElitesParameter {
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| 67 | get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
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| 68 | }
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| 69 | public LookupParameter<DoubleValue> ComparisonFactorParameter {
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| 70 | get { return (LookupParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
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| 71 | }
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| 72 | public LookupParameter<DoubleValue> CurrentSuccessRatioParameter {
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| 73 | get { return (LookupParameter<DoubleValue>)Parameters["CurrentSuccessRatio"]; }
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| 74 | }
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| 75 | public ValueLookupParameter<DoubleValue> SuccessRatioParameter {
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| 76 | get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
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| 77 | }
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| 78 | public LookupParameter<DoubleValue> SelectionPressureParameter {
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| 79 | get { return (LookupParameter<DoubleValue>)Parameters["SelectionPressure"]; }
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| 80 | }
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| 81 | public ValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
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| 82 | get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
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| 83 | }
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| 84 | public ValueLookupParameter<BoolValue> OffspringSelectionBeforeMutationParameter {
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| 85 | get { return (ValueLookupParameter<BoolValue>)Parameters["OffspringSelectionBeforeMutation"]; }
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| 86 | }
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| 87 | #endregion
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| 88 |
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| 89 | [StorableConstructor]
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| 90 | private OffspringSelectionGeneticAlgorithmMainOperator(bool deserializing) : base(deserializing) { }
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| 91 | private OffspringSelectionGeneticAlgorithmMainOperator(OffspringSelectionGeneticAlgorithmMainOperator original, Cloner cloner)
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| 92 | : base(original, cloner) {
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| 93 | }
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| 94 | public override IDeepCloneable Clone(Cloner cloner) {
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| 95 | return new OffspringSelectionGeneticAlgorithmMainOperator(this, cloner);
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| 96 | }
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| 97 | public OffspringSelectionGeneticAlgorithmMainOperator()
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| 98 | : base() {
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| 99 | Initialize();
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| 100 | }
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| 101 |
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| 102 | private void Initialize() {
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| 103 | #region Create parameters
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| 104 | Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
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| 105 | Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
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| 106 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
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| 107 | Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
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| 108 | Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
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| 109 | Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
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| 110 | Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
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| 111 | Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions."));
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| 112 | Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
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| 113 | Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
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| 114 | Parameters.Add(new LookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1]."));
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| 115 | Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
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| 116 | Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved."));
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| 117 | Parameters.Add(new LookupParameter<DoubleValue>("SelectionPressure", "The actual selection pressure."));
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| 118 | Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm."));
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| 119 | Parameters.Add(new ValueLookupParameter<BoolValue>("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation."));
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| 120 | #endregion
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| 121 |
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| 122 | #region Create operators
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| 123 | Placeholder selector = new Placeholder();
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| 124 | SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
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| 125 | ChildrenCreator childrenCreator = new ChildrenCreator();
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| 126 | UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
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| 127 | Placeholder crossover = new Placeholder();
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| 128 | ConditionalBranch osBeforeMutationBranch = new ConditionalBranch();
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| 129 | Placeholder evaluator1 = new Placeholder();
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| 130 | IntCounter evaluationCounter1 = new IntCounter();
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| 131 | WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator();
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| 132 | StochasticBranch mutationBranch1 = new StochasticBranch();
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| 133 | Placeholder mutator1 = new Placeholder();
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| 134 | Placeholder evaluator2 = new Placeholder();
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| 135 | IntCounter evaluationCounter2 = new IntCounter();
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| 136 | StochasticBranch mutationBranch2 = new StochasticBranch();
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| 137 | Placeholder mutator2 = new Placeholder();
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| 138 | Placeholder evaluator3 = new Placeholder();
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| 139 | IntCounter evaluationCounter3 = new IntCounter();
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| 140 | WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator();
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| 141 | SubScopesRemover subScopesRemover = new SubScopesRemover();
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| 142 | OffspringSelector offspringSelector = new OffspringSelector();
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| 143 | SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
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| 144 | BestSelector bestSelector = new BestSelector();
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| 145 | WorstSelector worstSelector = new WorstSelector();
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| 146 | RightReducer rightReducer = new RightReducer();
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| 147 | LeftReducer leftReducer = new LeftReducer();
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| 148 | MergingReducer mergingReducer = new MergingReducer();
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| 149 |
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| 150 | selector.Name = "Selector (placeholder)";
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| 151 | selector.OperatorParameter.ActualName = SelectorParameter.Name;
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| 152 |
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| 153 | childrenCreator.ParentsPerChild = new IntValue(2);
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| 154 |
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| 155 | crossover.Name = "Crossover (placeholder)";
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| 156 | crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
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| 157 |
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| 158 | osBeforeMutationBranch.Name = "Apply OS before mutation?";
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| 159 | osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
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| 160 |
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| 161 | evaluator1.Name = "Evaluator (placeholder)";
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| 162 | evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name;
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| 163 |
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| 164 | evaluationCounter1.Name = "EvaluatedSolutions++";
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| 165 | evaluationCounter1.Increment = new IntValue(1);
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| 166 | evaluationCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
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| 167 |
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| 168 | qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
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| 169 | qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name;
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| 170 | qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name;
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| 171 | qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name;
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| 172 | qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring";
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| 173 |
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| 174 | mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
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| 175 | mutationBranch1.RandomParameter.ActualName = RandomParameter.Name;
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| 176 |
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| 177 | mutator1.Name = "Mutator (placeholder)";
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| 178 | mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
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| 179 |
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| 180 | evaluator2.Name = "Evaluator (placeholder)";
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| 181 | evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
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| 182 |
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| 183 | evaluationCounter2.Name = "EvaluatedSolutions++";
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| 184 | evaluationCounter2.Increment = new IntValue(1);
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| 185 | evaluationCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
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| 186 |
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| 187 | mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
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| 188 | mutationBranch2.RandomParameter.ActualName = RandomParameter.Name;
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| 189 |
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| 190 | mutator2.Name = "Mutator (placeholder)";
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| 191 | mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
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| 192 |
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| 193 | evaluator3.Name = "Evaluator (placeholder)";
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| 194 | evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name;
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| 195 |
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| 196 | evaluationCounter3.Name = "EvaluatedSolutions++";
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| 197 | evaluationCounter3.Increment = new IntValue(1);
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| 198 | evaluationCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
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| 199 |
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| 200 | qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
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| 201 | qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name;
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| 202 | qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name;
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| 203 | qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name;
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| 204 | qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring";
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| 205 |
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| 206 | subScopesRemover.RemoveAllSubScopes = true;
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| 207 |
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| 208 | offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
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| 209 | offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
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| 210 | offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
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| 211 | offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
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| 212 | offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation";
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| 213 | offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners";
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| 214 | offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring";
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| 215 |
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| 216 | bestSelector.CopySelected = new BoolValue(false);
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| 217 | bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
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| 218 | bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
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| 219 | bestSelector.QualityParameter.ActualName = QualityParameter.Name;
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| 220 |
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| 221 | worstSelector.CopySelected = new BoolValue(false);
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| 222 | worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
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| 223 | worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
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| 224 | worstSelector.QualityParameter.ActualName = QualityParameter.Name;
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| 225 | #endregion
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| 226 |
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| 227 | #region Create operator graph
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| 228 | OperatorGraph.InitialOperator = selector;
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| 229 | selector.Successor = subScopesProcessor1;
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| 230 | subScopesProcessor1.Operators.Add(new EmptyOperator());
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| 231 | subScopesProcessor1.Operators.Add(childrenCreator);
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| 232 | subScopesProcessor1.Successor = offspringSelector;
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| 233 | childrenCreator.Successor = uniformSubScopesProcessor;
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| 234 | uniformSubScopesProcessor.Operator = crossover;
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| 235 | uniformSubScopesProcessor.Successor = null;
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| 236 | crossover.Successor = osBeforeMutationBranch;
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| 237 | osBeforeMutationBranch.TrueBranch = evaluator1;
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| 238 | osBeforeMutationBranch.FalseBranch = mutationBranch2;
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| 239 | osBeforeMutationBranch.Successor = subScopesRemover;
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| 240 | evaluator1.Successor = evaluationCounter1;
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| 241 | evaluationCounter1.Successor = qualityComparer1;
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| 242 | qualityComparer1.Successor = mutationBranch1;
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| 243 | mutationBranch1.FirstBranch = mutator1;
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| 244 | mutationBranch1.SecondBranch = null;
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| 245 | mutationBranch1.Successor = null;
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| 246 | mutator1.Successor = evaluator2;
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| 247 | evaluator2.Successor = evaluationCounter2;
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| 248 | evaluationCounter2.Successor = null;
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| 249 | mutationBranch2.FirstBranch = mutator2;
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| 250 | mutationBranch2.SecondBranch = null;
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| 251 | mutationBranch2.Successor = evaluator3;
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| 252 | mutator2.Successor = null;
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| 253 | evaluator3.Successor = evaluationCounter3;
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| 254 | evaluationCounter3.Successor = qualityComparer2;
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| 255 | subScopesRemover.Successor = null;
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| 256 | offspringSelector.OffspringCreator = selector;
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| 257 | offspringSelector.Successor = subScopesProcessor2;
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| 258 | subScopesProcessor2.Operators.Add(bestSelector);
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| 259 | subScopesProcessor2.Operators.Add(worstSelector);
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| 260 | subScopesProcessor2.Successor = mergingReducer;
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| 261 | bestSelector.Successor = rightReducer;
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| 262 | rightReducer.Successor = null;
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| 263 | worstSelector.Successor = leftReducer;
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| 264 | leftReducer.Successor = null;
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| 265 | mergingReducer.Successor = null;
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| 266 | #endregion
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| 267 | }
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| 268 |
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| 269 | public override IOperation Apply() {
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| 270 | if (CrossoverParameter.ActualValue == null)
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| 271 | return null;
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| 272 | return base.Apply();
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| 273 | }
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| 274 | }
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| 275 | }
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