| [17479] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 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 HEAL.Attic;
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| 29 | using HeuristicLab.Selection;
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| 30 | using HeuristicLab.Optimization;
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| 31 |
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| 32 | namespace HeuristicLab.Algorithms.SWGA {
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| 33 | /// <summary>
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| 34 | /// An operator which represents the main loop of a genetic algorithm.
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| 35 | /// </summary>
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| 36 | [Item("GeneticAlgorithmMainLoop", "An operator which represents the main loop of a genetic algorithm.")]
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| 37 | [StorableType("78FFFD80-9708-4FE7-9092-16DA0B51D33B")]
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| 38 | public sealed class GeneticAlgorithmMainLoop : AlgorithmOperator {
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| 39 | #region Parameter properties
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| 40 | public ValueLookupParameter<IRandom> RandomParameter {
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| 41 | get { return (ValueLookupParameter<IRandom>)Parameters["GlobalRandom"]; }
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| 42 | }
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| 43 | public ValueLookupParameter<BoolValue> MaximizationParameter {
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| 44 | get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
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| 45 | }
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| 46 | public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
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| 47 | get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
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| 48 | }
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| 49 | public ValueLookupParameter<IOperator> SelectorParameter {
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| 50 | get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
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| 51 | }
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| 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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| 55 | public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
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| 56 | get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
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| 57 | }
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| 58 | public ValueLookupParameter<IOperator> MutatorParameter {
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| 59 | get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
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| 60 | }
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| 61 | public ValueLookupParameter<IOperator> EvaluatorParameter {
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| 62 | get { return (ValueLookupParameter<IOperator>)Parameters["Evaluator"]; }
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| 63 | }
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| 64 | public ValueLookupParameter<IntValue> ElitesParameter {
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| 65 | get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
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| 66 | }
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| 67 | public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
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| 68 | get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
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| 69 | }
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| 70 | public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
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| 71 | get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
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| 72 | }
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| 73 | public ValueLookupParameter<VariableCollection> ResultsParameter {
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| 74 | get { return (ValueLookupParameter<VariableCollection>)Parameters["Results"]; }
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| 75 | }
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| 76 | public ValueLookupParameter<IOperator> AnalyzerParameter {
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| 77 | get { return (ValueLookupParameter<IOperator>)Parameters["Analyzer"]; }
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| 78 | }
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| 79 | public ValueLookupParameter<IntValue> EvaluatedSolutionsParameter {
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| 80 | get { return (ValueLookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
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| 81 | }
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| 82 | public ValueLookupParameter<IntValue> PopulationSizeParameter {
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| 83 | get { return (ValueLookupParameter<IntValue>)Parameters["PopulationSize"]; }
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| 84 | }
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| 85 | private ScopeParameter CurrentScopeParameter {
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| 86 | get { return (ScopeParameter)Parameters["CurrentScope"]; }
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| 87 | }
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| 88 |
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| 89 | public IScope CurrentScope {
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| 90 | get { return CurrentScopeParameter.ActualValue; }
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| 91 | }
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| 92 |
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| 93 | public IValueLookupParameter<IOperator> TerminatorParameter {
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| 94 | get { return (IValueLookupParameter<IOperator>)Parameters["Terminator"]; }
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| 95 | }
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| 96 |
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| 97 | public IValueLookupParameter<IOperator> SlidingWindowParameter {
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| 98 | get { return (IValueLookupParameter<IOperator>)Parameters["SlidingWindow"]; }
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| 99 | }
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| 100 | #endregion
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| 101 |
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| 102 | [StorableConstructor]
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| 103 | private GeneticAlgorithmMainLoop(StorableConstructorFlag _) : base(_) { }
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| 104 | private GeneticAlgorithmMainLoop(GeneticAlgorithmMainLoop original, Cloner cloner)
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| 105 | : base(original, cloner) {
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| 106 | }
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| 107 | public override IDeepCloneable Clone(Cloner cloner) {
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| 108 | return new GeneticAlgorithmMainLoop(this, cloner);
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| 109 | }
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| 110 | public GeneticAlgorithmMainLoop()
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| 111 | : base() {
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| 112 | Initialize();
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| 113 | }
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| 114 |
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| 115 | private void Initialize() {
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| 116 | #region Create parameters
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| 117 | Parameters.Add(new ValueLookupParameter<IRandom>("GlobalRandom", "A pseudo random number generator."));
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| 118 | Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
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| 119 | Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
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| 120 | Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
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| 121 | Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
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| 122 | Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
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| 123 | Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
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| 124 | Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
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| 125 | Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
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| 126 | Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
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| 127 | Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
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| 128 | Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
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| 129 | Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
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| 130 | Parameters.Add(new ValueLookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
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| 131 | Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The size of the population."));
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| 132 | 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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| 133 | Parameters.Add(new ValueLookupParameter<IOperator>("Terminator", "The termination criteria that defines if the algorithm should continue or stop"));
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| 134 | Parameters.Add(new ValueLookupParameter<IOperator>("SlidingWindow", "Operator which moves a sliding window over the training data."));
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| 135 | #endregion
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| 136 |
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| 137 | #region Create operators
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| 138 | VariableCreator variableCreator = new VariableCreator();
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| 139 | ResultsCollector resultsCollector1 = new ResultsCollector();
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| 140 | Placeholder analyzer1 = new Placeholder();
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| 141 | Placeholder slidingWindow = new Placeholder() { Name = "Sliding Window (Placeholder)" };
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| 142 | Placeholder selector = new Placeholder();
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| 143 | SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
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| 144 | ChildrenCreator childrenCreator = new ChildrenCreator();
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| 145 | UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
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| 146 | Placeholder crossover = new Placeholder();
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| 147 | StochasticBranch stochasticBranch = new StochasticBranch();
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| 148 | Placeholder mutator = new Placeholder();
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| 149 | SubScopesRemover subScopesRemover = new SubScopesRemover();
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| 150 | UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
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| 151 | Placeholder evaluator = new Placeholder();
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| 152 | SubScopesCounter subScopesCounter = new SubScopesCounter();
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| 153 | SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
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| 154 | BestSelector bestSelector = new BestSelector();
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| 155 | RightReducer rightReducer = new RightReducer();
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| 156 | MergingReducer mergingReducer = new MergingReducer();
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| 157 | IntCounter intCounter = new IntCounter();
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| 158 | Comparator comparator = new Comparator();
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| 159 | Placeholder analyzer2 = new Placeholder();
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| 160 | TerminationOperator termination = new TerminationOperator();
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| 161 | ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
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| 162 |
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| 163 | variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class GeneticAlgorithm expects this to be called Generations
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| 164 |
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| 165 | resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
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| 166 | resultsCollector1.ResultsParameter.ActualName = "Results";
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| 167 |
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| 168 | analyzer1.Name = "Analyzer";
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| 169 | analyzer1.OperatorParameter.ActualName = "Analyzer";
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| 170 |
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| 171 | selector.Name = "Selector";
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| 172 | selector.OperatorParameter.ActualName = "Selector";
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| 173 |
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| 174 | childrenCreator.ParentsPerChild = new IntValue(2);
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| 175 |
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| 176 | crossover.Name = "Crossover";
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| 177 | crossover.OperatorParameter.ActualName = "Crossover";
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| 178 |
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| 179 | stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability";
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| 180 | stochasticBranch.RandomParameter.ActualName = "Random";
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| 181 |
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| 182 | mutator.Name = "Mutator";
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| 183 | mutator.OperatorParameter.ActualName = "Mutator";
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| 184 |
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| 185 | subScopesRemover.RemoveAllSubScopes = true;
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| 186 |
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| 187 | uniformSubScopesProcessor2.Parallel.Value = true;
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| 188 |
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| 189 | evaluator.Name = "Evaluator";
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| 190 | evaluator.OperatorParameter.ActualName = "Evaluator";
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| 191 |
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| 192 | subScopesCounter.Name = "Increment EvaluatedSolutions";
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| 193 | subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
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| 194 |
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| 195 | bestSelector.CopySelected = new BoolValue(false);
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| 196 | bestSelector.MaximizationParameter.ActualName = "Maximization";
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| 197 | bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites";
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| 198 | bestSelector.QualityParameter.ActualName = "Quality";
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| 199 |
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| 200 | intCounter.Increment = new IntValue(1);
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| 201 | intCounter.ValueParameter.ActualName = "Generations";
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| 202 |
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| 203 | comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
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| 204 | comparator.LeftSideParameter.ActualName = "Generations";
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| 205 | comparator.ResultParameter.ActualName = "Terminate";
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| 206 | comparator.RightSideParameter.ActualName = "MaximumGenerations";
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| 207 |
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| 208 | analyzer2.Name = "Analyzer";
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| 209 | analyzer2.OperatorParameter.ActualName = "Analyzer";
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| 210 |
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| 211 | termination.TerminatorParameter.ActualName = TerminatorParameter.Name;
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| 212 | slidingWindow.OperatorParameter.ActualName = SlidingWindowParameter.Name;
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| 213 |
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| 214 |
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| 215 | reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
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| 216 | reevaluateElitesBranch.Name = "Reevaluate elites ?";
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| 217 | #endregion
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| 218 |
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| 219 | #region Create operator graph
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| 220 | OperatorGraph.InitialOperator = variableCreator;
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| 221 | variableCreator.Successor = resultsCollector1;
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| 222 | resultsCollector1.Successor = analyzer1;
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| 223 | analyzer1.Successor = slidingWindow;
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| 224 | slidingWindow.Successor = selector;
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| 225 | selector.Successor = subScopesProcessor1;
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| 226 | subScopesProcessor1.Operators.Add(new EmptyOperator());
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| 227 | subScopesProcessor1.Operators.Add(childrenCreator);
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| 228 | subScopesProcessor1.Successor = subScopesProcessor2;
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| 229 | childrenCreator.Successor = uniformSubScopesProcessor1;
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| 230 | uniformSubScopesProcessor1.Operator = crossover;
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| 231 | uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
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| 232 | crossover.Successor = stochasticBranch;
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| 233 | stochasticBranch.FirstBranch = mutator;
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| 234 | stochasticBranch.SecondBranch = null;
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| 235 | stochasticBranch.Successor = subScopesRemover;
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| 236 | mutator.Successor = null;
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| 237 | subScopesRemover.Successor = null;
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| 238 | uniformSubScopesProcessor2.Operator = evaluator;
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| 239 | uniformSubScopesProcessor2.Successor = subScopesCounter;
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| 240 | evaluator.Successor = null;
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| 241 | subScopesCounter.Successor = null;
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| 242 | subScopesProcessor2.Operators.Add(bestSelector);
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| 243 | subScopesProcessor2.Operators.Add(new EmptyOperator());
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| 244 | subScopesProcessor2.Successor = mergingReducer;
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| 245 | bestSelector.Successor = rightReducer;
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| 246 | rightReducer.Successor = reevaluateElitesBranch;
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| 247 | reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor2;
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| 248 | reevaluateElitesBranch.FalseBranch = null;
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| 249 | reevaluateElitesBranch.Successor = null;
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| 250 | mergingReducer.Successor = intCounter;
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| 251 | intCounter.Successor = comparator;
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| 252 | comparator.Successor = analyzer2;
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| 253 | analyzer2.Successor = termination;
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| 254 | termination.ContinueBranch = slidingWindow;
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| 255 | #endregion
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| 256 | }
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| 257 |
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| 258 | [StorableHook(HookType.AfterDeserialization)]
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| 259 | private void AfterDeserialization() {
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| 260 | // BackwardsCompatibility3.3
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| 261 | #region Backwards compatible code, remove with 3.4
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| 262 | if (!Parameters.ContainsKey("ReevaluateElites")) {
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| 263 | Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
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| 264 | }
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| 265 | #endregion
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| 266 | }
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| 267 |
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| 268 | public override IOperation Apply() {
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| 269 | if (CrossoverParameter.ActualValue == null)
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| 270 | return null;
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| 271 | return base.Apply();
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| 272 | }
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| 273 | }
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| 274 | }
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