source: branches/ALPS/HeuristicLab.Algorithms.ALPS/3.3/AlpsGeneticAlgorithmMainLoop.cs @ 11585

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 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 ILookupParameter<IRandom> RandomParameter {
      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
    }
    /*public ILookupParameter<BoolValue> MaximizationParameter {
      get { return (ILookupParameter<BoolValue>)Parameters["Maximization"]; }
    }
    public IScopeTreeLookupParameter<DoubleValue> QualityParameter {
      get { return (IScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public ILookupParameter<DoubleValue> BestKnownQualityParameter {
      get { return (ILookupParameter<DoubleValue>)Parameters["BestKnownQuality"]; }
    }
    public ILookupParameter<IOperator> EvaluatorParameter {
      get { return (ILookupParameter<IOperator>)Parameters["Evaluator"]; }
    }*/
    public ILookupParameter<IntValue> PopulationSizeParameter {
      get { return (ILookupParameter<IntValue>)Parameters["PopulationSize"]; }
    }
    public ILookupParameter<IntValue> MaximumGenerationsParameter {
      get { return (ILookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
    }
    public ILookupParameter<IOperator> SelectorParameter {
      get { return (ILookupParameter<IOperator>)Parameters["Selector"]; }
    }
    public ILookupParameter<IOperator> CrossoverParameter {
      get { return (ILookupParameter<IOperator>)Parameters["Crossover"]; }
    }
    public ILookupParameter<PercentValue> MutationProbabilityParameter {
      get { return (ILookupParameter<PercentValue>)Parameters["MutationProbability"]; }
    }
    public ILookupParameter<IOperator> MutatorParameter {
      get { return (ILookupParameter<IOperator>)Parameters["Mutator"]; }
    }
    public ILookupParameter<IntValue> ElitesParameter {
      get { return (ILookupParameter<IntValue>)Parameters["Elites"]; }
    }
    public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
      get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
    }
    public ILookupParameter<ResultCollection> ResultsParameter {
      get { return (ILookupParameter<ResultCollection>)Parameters["Results"]; }
    }
    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(); }
    }

    [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 LookupParameter<IRandom>("Random", "A pseudo random number generator."));
      /*Parameters.Add(new LookupParameter<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<DoubleValue>("BestKnownQuality", "The best known quality value found so far."));
      Parameters.Add(new LookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions."));*/
      Parameters.Add(new LookupParameter<IntValue>("PopulationSize", "The size of the population of solutions."));
      Parameters.Add(new LookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations that the algorithm should process."));
      Parameters.Add(new LookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
      Parameters.Add(new LookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
      Parameters.Add(new LookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
      Parameters.Add(new LookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
      Parameters.Add(new LookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
      Parameters.Add(new LookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
      Parameters.Add(new LookupParameter<ResultCollection>("Results", "The results collection to store the results."));
      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 resultsCollector = new ResultsCollector();
      var initAnalyzerPlaceholder = new Placeholder() { Name = "Analyzer (Placeholder)" };
      var initLayerAnalyzerProcessor = new SubScopesProcessor();
      var initLayerAnalyzerPlaceholder = new Placeholder() { Name = "LayerAnalyzer (Placeholder)" };
      var matingPoolCreator = new MatingPoolCreator() { Name = "Create Mating Pools" };
      var matingPoolProcessor = new UniformSubScopesProcessor();
      var mainOperator = PrepareGeneticAlgorithmMainLoop();
      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 CombinedOperator() { 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.Successor = resultsCollector;

      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
      resultsCollector.CollectedValues.Add(new ScopeTreeLookupParameter<ResultCollection>("LayerResults", "Result set for each layer", "Results"));
      resultsCollector.Successor = initAnalyzerPlaceholder;

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

      initLayerAnalyzerProcessor.Operators.Add(initLayerAnalyzerPlaceholder);
      initLayerAnalyzerProcessor.Successor = matingPoolCreator;

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

      matingPoolCreator.Successor = matingPoolProcessor;

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

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

      evaluatedSolutionsReducer.ParameterToReduce.ActualName = "EvaluatedSolutions";
      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 = matingPoolCreator;
    }

    private GeneticAlgorithmMainLoop PrepareGeneticAlgorithmMainLoop() {
      var mainLoop = new GeneticAlgorithmMainLoop();
      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 elitesMerger = mainLoop.OperatorGraph.Iterate().OfType<MergingReducer>().First();

      // Operator starts with selector
      mainLoop.OperatorGraph.InitialOperator = 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 = new ReductionOperation(ReductionOperations.Max);
      ageCalculator.TargetOperation.Value = new ReductionOperation(ReductionOperations.Assign);
      crossover.Successor = ageCalculator;
      ageCalculator.Successor = crossoverSuccessor;

      // 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";

      // Remove unnessesary subtrees
      //foreach (var @operator in mainLoop.OperatorGraph.Operators.OfType<SingleSuccessorOperator>().Where(o => o.Successor == selector))
      //  @operator.Successor = null;

      return mainLoop;
    }
  }
}