source: branches/LearningClassifierSystems/HeuristicLab.Algorithms.LearningClassifierSystems/3.3/LCSAdaptedGeneticAlgorithm.cs @ 9154

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.ConditionActionEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Selection;
namespace HeuristicLab.Algorithms.LearningClassifierSystems {
  /// <summary>
  /// An operator which represents the main loop of a genetic algorithm.
  /// </summary>
  [Item("LCSAdaptedGeneticAlgorithm", "An operator which represents the main loop of a genetic algorithm, which has been adapdet for learning classifier systems.")]
  [StorableClass]
  public sealed class LCSAdaptedGeneticAlgorithm : AlgorithmOperator {
    private const string TEMPID = "TempID";
    private const string SUBSUMEDBY = "SubsumedBy";
    private const string SUBSUMED = "Subsumed";

    #region Parameter properties
    public ValueLookupParameter<IRandom> RandomParameter {
      get { return (ValueLookupParameter<IRandom>)Parameters["Random"]; }
    }
    public ValueLookupParameter<BoolValue> MaximizationParameter {
      get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
    }
    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
    }
    public ValueLookupParameter<IOperator> SelectorParameter {
      get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
    }
    public ValueLookupParameter<IOperator> CrossoverParameter {
      get { return (ValueLookupParameter<IOperator>)Parameters["Crossover"]; }
    }
    public ValueLookupParameter<IOperator> AfterCrossoverParameter {
      get { return (ValueLookupParameter<IOperator>)Parameters["AfterCrossover"]; }
    }
    public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
      get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
    }
    public ValueLookupParameter<IOperator> MutatorParameter {
      get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
    }
    public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
      get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
    }
    public ValueLookupParameter<VariableCollection> ResultsParameter {
      get { return (ValueLookupParameter<VariableCollection>)Parameters["Results"]; }
    }
    public ValueLookupParameter<IntValue> EvaluatedSolutionsParameter {
      get { return (ValueLookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
    }
    public ValueLookupParameter<IntValue> PopulationSizeParameter {
      get { return (ValueLookupParameter<IntValue>)Parameters["PopulationSize"]; }
    }
    public ValueLookupParameter<BoolValue> DoGASubsumptionParameter {
      get { return (ValueLookupParameter<BoolValue>)Parameters["DoGASubsumption"]; }
    }
    private ScopeParameter CurrentScopeParameter {
      get { return (ScopeParameter)Parameters["CurrentScope"]; }
    }

    public IScope CurrentScope {
      get { return CurrentScopeParameter.ActualValue; }
    }
    #endregion

    [StorableConstructor]
    private LCSAdaptedGeneticAlgorithm(bool deserializing) : base(deserializing) { }
    private LCSAdaptedGeneticAlgorithm(LCSAdaptedGeneticAlgorithm original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new LCSAdaptedGeneticAlgorithm(this, cloner);
    }
    public LCSAdaptedGeneticAlgorithm()
      : base() {
      Initialize();
    }

    private void Initialize() {
      #region Create parameters
      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
      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 ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
      Parameters.Add(new ValueLookupParameter<IOperator>("AfterCrossover", "The operator executed after crossing the solutions."));
      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
      Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
      Parameters.Add(new ValueLookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
      Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The size of the population."));
      Parameters.Add(new ValueLookupParameter<BoolValue>("DoGASubsumption", "Sets if GA subsumption is executed."));
      Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied."));
      #endregion

      #region Create operators
      VariableCreator variableCreator = new VariableCreator();
      ResultsCollector resultsCollector1 = new ResultsCollector();
      Placeholder selector = new Placeholder();
      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
      ChildrenCreator childrenCreator = new ChildrenCreator();
      UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
      Placeholder crossover = new Placeholder();
      Placeholder afterCrossover = new Placeholder();
      Placeholder mutator = new Placeholder();
      SubScopesRemover subScopesRemover = new SubScopesRemover();
      SubScopesCounter subScopesCounter = new SubScopesCounter();
      MergingReducer mergingReducer = new MergingReducer();
      IntCounter intCounter = new IntCounter();
      Comparator comparator = new Comparator();
      ConditionalBranch conditionalBranch = new ConditionalBranch();

      TempSubScopeIDAssigner tempIdAssigner = new TempSubScopeIDAssigner();
      ConditionalBranch doGASubsumptionBranch1 = new ConditionalBranch();
      UniformSubScopesProcessor setSubsumptionFalseSubScopesProcessor = new UniformSubScopesProcessor();
      Assigner setSubsumpByAssigner = new Assigner();
      Assigner setSubsumptionFalseAssigner = new Assigner();
      CheckGASubsumptionOperator checkGASubsumptionOperator = new CheckGASubsumptionOperator();
      ConditionalBranch doGASubsumptionBranch2 = new ConditionalBranch();
      ExecuteGASubsumptionOperator executeGAsubsumptionOperator = new ExecuteGASubsumptionOperator();
      ConditionalSelector subsumptionSelector = new ConditionalSelector();
      LeftReducer subsumptionLeftReducer = new LeftReducer();

      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class GeneticAlgorithm expects this to be called Generations

      //resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Iterations"));
      resultsCollector1.ResultsParameter.ActualName = "Results";

      tempIdAssigner.LeftSideParameter.ActualName = TEMPID;

      setSubsumpByAssigner.LeftSideParameter.ActualName = SUBSUMEDBY;
      setSubsumpByAssigner.RightSideParameter.Value = new IntValue(-1);

      setSubsumptionFalseAssigner.LeftSideParameter.ActualName = SUBSUMED;
      setSubsumptionFalseAssigner.RightSideParameter.Value = new BoolValue(false);

      selector.Name = "Selector";
      selector.OperatorParameter.ActualName = "Selector";

      childrenCreator.ParentsPerChild = new IntValue(2);

      crossover.Name = "Crossover";
      crossover.OperatorParameter.ActualName = "Crossover";

      afterCrossover.Name = "AfterCrossover";
      afterCrossover.OperatorParameter.ActualName = "AfterCrossover";

      mutator.Name = "Mutator";
      mutator.OperatorParameter.ActualName = "Mutator";

      doGASubsumptionBranch1.ConditionParameter.ActualName = DoGASubsumptionParameter.ActualName;

      checkGASubsumptionOperator.ChildClassifiersParameter.ActualName = "CombinedIntegerVector";
      checkGASubsumptionOperator.ParentsClassifiersParameter.ActualName = "CombinedIntegerVector";
      checkGASubsumptionOperator.NumerositiesParameter.ActualName = "Numerosity";
      checkGASubsumptionOperator.ExperiencesParameter.ActualName = "Experience";
      checkGASubsumptionOperator.ErrorsParameter.ActualName = "Error";
      checkGASubsumptionOperator.TempIDParameter.ActualName = TEMPID;
      checkGASubsumptionOperator.ErrorZeroParameter.ActualName = "ErrorZero";
      checkGASubsumptionOperator.ThetaSubsumptionParameter.ActualName = "ThetaSubsumption";
      checkGASubsumptionOperator.SubsumedByParameter.ActualName = SUBSUMEDBY;
      checkGASubsumptionOperator.SubsumedParameter.ActualName = SUBSUMED;

      subScopesRemover.RemoveAllSubScopes = true;

      doGASubsumptionBranch2.ConditionParameter.ActualName = DoGASubsumptionParameter.ActualName;

      executeGAsubsumptionOperator.NumerositiesParameter.ActualName = "Numerosity";
      executeGAsubsumptionOperator.TempIDParameter.ActualName = TEMPID;
      executeGAsubsumptionOperator.SubsumedByParameter.ActualName = SUBSUMEDBY;

      subsumptionSelector.ConditionParameter.ActualName = SUBSUMED;
      subsumptionSelector.CopySelected = new BoolValue(false);

      subScopesCounter.Name = "Increment EvaluatedSolutions";
      subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;

      intCounter.Increment = new IntValue(1);
      intCounter.ValueParameter.ActualName = "Generations";

      comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
      comparator.LeftSideParameter.ActualName = "Generations";
      comparator.ResultParameter.ActualName = "Terminate";
      comparator.RightSideParameter.ActualName = "MaximumGenerations";

      conditionalBranch.ConditionParameter.ActualName = "Terminate";
      #endregion

      #region Create operator graph
      OperatorGraph.InitialOperator = variableCreator;
      variableCreator.Successor = resultsCollector1;
      resultsCollector1.Successor = tempIdAssigner;
      tempIdAssigner.Successor = setSubsumptionFalseSubScopesProcessor;
      setSubsumptionFalseSubScopesProcessor.Operator = setSubsumpByAssigner;
      setSubsumpByAssigner.Successor = setSubsumptionFalseAssigner;
      setSubsumptionFalseAssigner.Successor = null;
      setSubsumptionFalseSubScopesProcessor.Successor = selector;
      selector.Successor = subScopesProcessor1;
      subScopesProcessor1.Operators.Add(new EmptyOperator());
      subScopesProcessor1.Operators.Add(childrenCreator);
      subScopesProcessor1.Successor = mergingReducer;
      childrenCreator.Successor = uniformSubScopesProcessor1;
      uniformSubScopesProcessor1.Operator = crossover;
      uniformSubScopesProcessor1.Successor = subScopesCounter;
      crossover.Successor = afterCrossover;
      afterCrossover.Successor = mutator;
      mutator.Successor = doGASubsumptionBranch1;
      doGASubsumptionBranch1.TrueBranch = checkGASubsumptionOperator;
      doGASubsumptionBranch1.FalseBranch = new EmptyOperator();
      doGASubsumptionBranch1.Successor = subScopesRemover;
      subScopesRemover.Successor = null;
      subScopesCounter.Successor = null;
      mergingReducer.Successor = doGASubsumptionBranch2;
      doGASubsumptionBranch2.TrueBranch = executeGAsubsumptionOperator;
      doGASubsumptionBranch2.FalseBranch = new EmptyOperator();
      executeGAsubsumptionOperator.Successor = subsumptionSelector;
      subsumptionSelector.Successor = subsumptionLeftReducer;
      subsumptionLeftReducer.Successor = null;
      doGASubsumptionBranch2.Successor = intCounter;
      intCounter.Successor = comparator;
      comparator.Successor = conditionalBranch;
      conditionalBranch.FalseBranch = selector;
      conditionalBranch.TrueBranch = null;
      conditionalBranch.Successor = null;
      #endregion
    }

    public override IOperation Apply() {
      if (CrossoverParameter.ActualValue == null)
        return null;
      return base.Apply();
    }
  }
}