source: branches/ALPS/HeuristicLab.Algorithms.ALPS/3.3/AlpsGeneticAlgorithmMainLoop.cs @ 12093

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Linq;
using HeuristicLab.Algorithms.GeneticAlgorithm;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Selection;

namespace HeuristicLab.Algorithms.ALPS {

  [Item("AlpsGeneticAlgorithmMainLoop", "An ALPS genetic algorithm main loop operator.")]
  [StorableClass]
  public sealed class AlpsGeneticAlgorithmMainLoop : AlgorithmOperator {
    #region Parameter Properties
    public ValueLookupParameter<BoolValue> MaximizationParameter {
      get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
    }
    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public ILookupParameter<IntValue> MaximumGenerationsParameter {
      get { return (ILookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
    }
    public ILookupParameter<IOperator> AnalyzerParameter {
      get { return (ILookupParameter<IOperator>)Parameters["Analyzer"]; }
    }
    public ILookupParameter<IOperator> LayerAnalyzerParameter {
      get { return (ILookupParameter<IOperator>)Parameters["LayerAnalyzer"]; }
    }
    #endregion

    public GeneticAlgorithmMainLoop MainOperator {
      get { return OperatorGraph.Iterate().OfType<GeneticAlgorithmMainLoop>().First(); }
    }
    public EldersEmigrator EldersEmigrator {
      get { return OperatorGraph.Iterate().OfType<EldersEmigrator>().First(); }
    }
    public LayerUpdator LayerUpdator {
      get { return OperatorGraph.Iterate().OfType<LayerUpdator>().First(); }
    }

    [StorableConstructor]
    private AlpsGeneticAlgorithmMainLoop(bool deserializing)
      : base(deserializing) { }
    private AlpsGeneticAlgorithmMainLoop(AlpsGeneticAlgorithmMainLoop original, Cloner cloner)
      : base(original, cloner) { }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new AlpsGeneticAlgorithmMainLoop(this, cloner);
    }
    public AlpsGeneticAlgorithmMainLoop()
      : base() {
      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
      Parameters.Add(new LookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations that the algorithm should process."));
      Parameters.Add(new LookupParameter<IOperator>("Analyzer", "The operator used to the analyze all individuals."));
      Parameters.Add(new LookupParameter<IOperator>("LayerAnalyzer", "The operator used to analyze each layer."));

      var variableCreator = new VariableCreator() { Name = "Initialize" };
      var initLayerAnalyzerProcessor = new SubScopesProcessor();
      var layerVariableCreator = new VariableCreator() { Name = "Initialize Layer" };
      var initLayerAnalyzerPlaceholder = new Placeholder() { Name = "LayerAnalyzer (Placeholder)" };
      var initAnalyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" };
      var resultsCollector = new ResultsCollector();
      var matingPoolPreProcessor = new UniformSubScopesProcessor() { Name = "MatingPoolPreProcessor" };
      var matingPoolPreSorter = new SubScopesSorter() { Name = "MatingPoolPreSorter" };
      var matingPoolCreator = new MatingPoolCreator() { Name = "Create Mating Pools" };
      var matingPoolProcessor = new LayerUniformSubScopesProcessor();
      var initializeLayer = new Assigner() { Name = "Reset LayerEvaluatedSolutions" };
      var mainOperator = CreatePreparedGeneticAlgorithmMainLoop();
      var layerAnalyzerPlaceholder = new Placeholder() { Name = "LayerAnalyzer (Placeholder)" };
      var generationsIcrementor = new IntCounter() { Name = "Increment Generations" };
      var evaluatedSolutionsReducer = new DataReducer() { Name = "Increment EvaluatedSolutions" };
      var eldersEmigrator = new EldersEmigrator() { Name = "Emigrate Elders" };
      var layerUpdator = new LayerUpdator(mainOperator) { Name = "Update Layers" };
      var analyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" };
      var generationsComparator = new Comparator() { Name = "Generations >= MaximumGenerations" };
      var terminateBranch = new ConditionalBranch() { Name = "Terminate?" };

      OperatorGraph.InitialOperator = variableCreator;

      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0)));
      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("GenerationsSinceLastRefresh", new IntValue(0)));
      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("OpenLayers", new IntValue(1)));
      variableCreator.Successor = initLayerAnalyzerProcessor;

      initLayerAnalyzerProcessor.Operators.Add(layerVariableCreator);
      initLayerAnalyzerProcessor.Successor = initAnalyzerPlaceholder;

      layerVariableCreator.CollectedValues.Add(new ValueParameter<IntValue>("LayerEvaluatedSolutions"));
      layerVariableCreator.CollectedValues.Add(new ValueParameter<ResultCollection>("LayerResults"));
      layerVariableCreator.Successor = initLayerAnalyzerPlaceholder;

      initLayerAnalyzerPlaceholder.OperatorParameter.ActualName = LayerAnalyzerParameter.Name;
      initLayerAnalyzerPlaceholder.Successor = null;

      initAnalyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name;
      initAnalyzerPlaceholder.Successor = resultsCollector;

      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
      resultsCollector.CollectedValues.Add(new ScopeTreeLookupParameter<ResultCollection>("LayerResults", "Result set for each layer", "LayerResults"));
      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("OpenLayers"));
      resultsCollector.CopyValue = new BoolValue(false);
      resultsCollector.Successor = matingPoolPreProcessor;

      matingPoolPreProcessor.Operator = matingPoolPreSorter;
      matingPoolPreProcessor.Successor = matingPoolCreator;

      matingPoolPreSorter.ValueParameter.ActualName = QualityParameter.Name;
      matingPoolPreSorter.DescendingParameter.ActualName = MaximizationParameter.Name;

      matingPoolCreator.Successor = matingPoolProcessor;

      matingPoolProcessor.Parallel.Value = true;
      matingPoolProcessor.Operator = initializeLayer;
      matingPoolProcessor.Successor = generationsIcrementor;

      initializeLayer.LeftSideParameter.ActualName = "LayerEvaluatedSolutions";
      initializeLayer.RightSideParameter.Value = new IntValue(0);
      initializeLayer.Successor = mainOperator;

      generationsIcrementor.ValueParameter.ActualName = "Generations";
      generationsIcrementor.Increment = new IntValue(1);
      generationsIcrementor.Successor = evaluatedSolutionsReducer;

      evaluatedSolutionsReducer.ParameterToReduce.ActualName = "LayerEvaluatedSolutions";
      evaluatedSolutionsReducer.TargetParameter.ActualName = "EvaluatedSolutions";
      evaluatedSolutionsReducer.ReductionOperation.Value = new ReductionOperation(ReductionOperations.Sum);
      evaluatedSolutionsReducer.TargetOperation.Value = new ReductionOperation(ReductionOperations.Sum);
      evaluatedSolutionsReducer.Successor = eldersEmigrator;

      mainOperator.Successor = layerAnalyzerPlaceholder;

      layerAnalyzerPlaceholder.OperatorParameter.ActualName = LayerAnalyzerParameter.Name;
      layerAnalyzerPlaceholder.Successor = null;

      eldersEmigrator.Successor = layerUpdator;

      layerUpdator.Successor = analyzerPlaceholder;

      analyzerPlaceholder.OperatorParameter.ActualName = AnalyzerParameter.Name;
      analyzerPlaceholder.Successor = generationsComparator;

      generationsComparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
      generationsComparator.LeftSideParameter.ActualName = "Generations";
      generationsComparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
      generationsComparator.ResultParameter.ActualName = "TerminateGenerations";
      generationsComparator.Successor = terminateBranch;

      terminateBranch.ConditionParameter.ActualName = "TerminateGenerations";
      terminateBranch.FalseBranch = matingPoolPreProcessor;
    }

    private GeneticAlgorithmMainLoop CreatePreparedGeneticAlgorithmMainLoop() {
      var mainLoop = new GeneticAlgorithmMainLoop();
      var numberOfSelectedSubScopesCalculator = new NumberOfSelectedSubScopesCalculator();
      var selector = mainLoop.OperatorGraph.Iterate().OfType<Placeholder>().First(o => o.OperatorParameter.ActualName == "Selector");
      var crossover = mainLoop.OperatorGraph.Iterate().OfType<Placeholder>().First(o => o.OperatorParameter.ActualName == "Crossover");
      var subScopesCounter = mainLoop.OperatorGraph.Iterate().OfType<SubScopesCounter>().First();
      var elitesMerger = mainLoop.OperatorGraph.Iterate().OfType<MergingReducer>().First();

      // Operator starts with numberOfSelectedSubScopesCalculator
      mainLoop.OperatorGraph.InitialOperator = numberOfSelectedSubScopesCalculator;
      numberOfSelectedSubScopesCalculator.Successor = selector;

      // Insert AgeCalculator between crossover and its successor
      var crossoverSuccessor = crossover.Successor;
      var ageCalculator = new DataReducer() { Name = "Calculate Age" };
      ageCalculator.ParameterToReduce.ActualName = "Age";
      ageCalculator.TargetParameter.ActualName = "Age";
      ageCalculator.ReductionOperation.Value = null;
      ageCalculator.ReductionOperation.ActualName = "AgeInheritance";
      ageCalculator.TargetOperation.Value = new ReductionOperation(ReductionOperations.Assign);
      crossover.Successor = ageCalculator;
      ageCalculator.Successor = crossoverSuccessor;

      // When counting the evaluated solutions, write in LayerEvaluatedSolutions
      subScopesCounter.ValueParameter.ActualName = "LayerEvaluatedSolutions";
      subScopesCounter.AccumulateParameter.Value = new BoolValue(false);

      // Instead of generational loop after merging of elites, increment ages of all individuals
      var processor = new UniformSubScopesProcessor();
      var incrementor = new IntCounter() { Name = "Increment Age" };
      processor.Operator = incrementor;
      processor.Successor = null;
      incrementor.ValueParameter.ActualName = "Age";
      incrementor.Increment = new IntValue(1);
      incrementor.Successor = null;
      elitesMerger.Successor = processor;

      // Parameterize
      foreach (var stochasticOperator in mainLoop.OperatorGraph.Iterate().OfType<IStochasticOperator>())
        stochasticOperator.RandomParameter.ActualName = "LocalRandom";
      foreach (var stochasticBranch in mainLoop.OperatorGraph.Iterate().OfType<StochasticBranch>())
        stochasticBranch.RandomParameter.ActualName = "LocalRandom";

      return mainLoop;
    }
  }
}