#region License Information
/* HeuristicLab
* Copyright (C) 2002-2010 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 .
*/
#endregion
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Selection;
namespace HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm {
///
/// An operator which represents the main loop of an offspring selection genetic algorithm.
///
[Item("OffspringSelectionGeneticAlgorithmMainOperator", "An operator that represents the core of an offspring selection genetic algorithm.")]
[StorableClass]
public sealed class OffspringSelectionGeneticAlgorithmMainOperator : AlgorithmOperator {
#region Parameter properties
public ValueLookupParameter RandomParameter {
get { return (ValueLookupParameter)Parameters["Random"]; }
}
public ValueLookupParameter MaximizationParameter {
get { return (ValueLookupParameter)Parameters["Maximization"]; }
}
public ScopeTreeLookupParameter QualityParameter {
get { return (ScopeTreeLookupParameter)Parameters["Quality"]; }
}
public ValueLookupParameter SelectorParameter {
get { return (ValueLookupParameter)Parameters["Selector"]; }
}
public ValueLookupParameter CrossoverParameter {
get { return (ValueLookupParameter)Parameters["Crossover"]; }
}
public ValueLookupParameter MutationProbabilityParameter {
get { return (ValueLookupParameter)Parameters["MutationProbability"]; }
}
public ValueLookupParameter MutatorParameter {
get { return (ValueLookupParameter)Parameters["Mutator"]; }
}
public ValueLookupParameter EvaluatorParameter {
get { return (ValueLookupParameter)Parameters["Evaluator"]; }
}
public LookupParameter EvaluatedSolutionsParameter {
get { return (LookupParameter)Parameters["EvaluatedSolutions"]; }
}
public ValueLookupParameter ElitesParameter {
get { return (ValueLookupParameter)Parameters["Elites"]; }
}
public LookupParameter ComparisonFactorParameter {
get { return (LookupParameter)Parameters["ComparisonFactor"]; }
}
public LookupParameter CurrentSuccessRatioParameter {
get { return (LookupParameter)Parameters["CurrentSuccessRatio"]; }
}
public ValueLookupParameter SuccessRatioParameter {
get { return (ValueLookupParameter)Parameters["SuccessRatio"]; }
}
public LookupParameter SelectionPressureParameter {
get { return (LookupParameter)Parameters["SelectionPressure"]; }
}
public ValueLookupParameter MaximumSelectionPressureParameter {
get { return (ValueLookupParameter)Parameters["MaximumSelectionPressure"]; }
}
public ValueLookupParameter OffspringSelectionBeforeMutationParameter {
get { return (ValueLookupParameter)Parameters["OffspringSelectionBeforeMutation"]; }
}
#endregion
[StorableConstructor]
private OffspringSelectionGeneticAlgorithmMainOperator(bool deserializing) : base(deserializing) { }
private OffspringSelectionGeneticAlgorithmMainOperator(OffspringSelectionGeneticAlgorithmMainOperator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new OffspringSelectionGeneticAlgorithmMainOperator(this, cloner);
}
public OffspringSelectionGeneticAlgorithmMainOperator()
: base() {
Initialize();
}
private void Initialize() {
#region Create parameters
Parameters.Add(new ValueLookupParameter("Random", "A pseudo random number generator."));
Parameters.Add(new ValueLookupParameter("Maximization", "True if the problem is a maximization problem, otherwise false."));
Parameters.Add(new ScopeTreeLookupParameter("Quality", "The value which represents the quality of a solution."));
Parameters.Add(new ValueLookupParameter("Selector", "The operator used to select solutions for reproduction."));
Parameters.Add(new ValueLookupParameter("Crossover", "The operator used to cross solutions."));
Parameters.Add(new ValueLookupParameter("MutationProbability", "The probability that the mutation operator is applied on a solution."));
Parameters.Add(new ValueLookupParameter("Mutator", "The operator used to mutate solutions."));
Parameters.Add(new ValueLookupParameter("Evaluator", "The operator used to evaluate solutions."));
Parameters.Add(new LookupParameter("EvaluatedSolutions", "The number of evaluated solutions."));
Parameters.Add(new ValueLookupParameter("Elites", "The numer of elite solutions which are kept in each generation."));
Parameters.Add(new LookupParameter("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]."));
Parameters.Add(new LookupParameter("CurrentSuccessRatio", "The current success ratio."));
Parameters.Add(new ValueLookupParameter("SuccessRatio", "The ratio of successful to total children that should be achieved."));
Parameters.Add(new LookupParameter("SelectionPressure", "The actual selection pressure."));
Parameters.Add(new ValueLookupParameter("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm."));
Parameters.Add(new ValueLookupParameter("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation."));
#endregion
#region Create operators
Placeholder selector = new Placeholder();
SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
ChildrenCreator childrenCreator = new ChildrenCreator();
UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
Placeholder crossover = new Placeholder();
ConditionalBranch osBeforeMutationBranch = new ConditionalBranch();
Placeholder evaluator1 = new Placeholder();
IntCounter evaluationCounter1 = new IntCounter();
WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator();
StochasticBranch mutationBranch1 = new StochasticBranch();
Placeholder mutator1 = new Placeholder();
Placeholder evaluator2 = new Placeholder();
IntCounter evaluationCounter2 = new IntCounter();
StochasticBranch mutationBranch2 = new StochasticBranch();
Placeholder mutator2 = new Placeholder();
Placeholder evaluator3 = new Placeholder();
IntCounter evaluationCounter3 = new IntCounter();
WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator();
SubScopesRemover subScopesRemover = new SubScopesRemover();
OffspringSelector offspringSelector = new OffspringSelector();
SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
BestSelector bestSelector = new BestSelector();
WorstSelector worstSelector = new WorstSelector();
RightReducer rightReducer = new RightReducer();
LeftReducer leftReducer = new LeftReducer();
MergingReducer mergingReducer = new MergingReducer();
selector.Name = "Selector (placeholder)";
selector.OperatorParameter.ActualName = SelectorParameter.Name;
childrenCreator.ParentsPerChild = new IntValue(2);
crossover.Name = "Crossover (placeholder)";
crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
osBeforeMutationBranch.Name = "Apply OS before mutation?";
osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
evaluator1.Name = "Evaluator (placeholder)";
evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluationCounter1.Name = "EvaluatedSolutions++";
evaluationCounter1.Increment = new IntValue(1);
evaluationCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name;
qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name;
qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name;
qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring";
mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mutationBranch1.RandomParameter.ActualName = RandomParameter.Name;
mutator1.Name = "Mutator (placeholder)";
mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
evaluator2.Name = "Evaluator (placeholder)";
evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluationCounter2.Name = "EvaluatedSolutions++";
evaluationCounter2.Increment = new IntValue(1);
evaluationCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
mutationBranch2.RandomParameter.ActualName = RandomParameter.Name;
mutator2.Name = "Mutator (placeholder)";
mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
evaluator3.Name = "Evaluator (placeholder)";
evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name;
evaluationCounter3.Name = "EvaluatedSolutions++";
evaluationCounter3.Increment = new IntValue(1);
evaluationCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name;
qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name;
qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name;
qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring";
subScopesRemover.RemoveAllSubScopes = true;
offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation";
offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners";
offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring";
bestSelector.CopySelected = new BoolValue(false);
bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
bestSelector.QualityParameter.ActualName = QualityParameter.Name;
worstSelector.CopySelected = new BoolValue(false);
worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
worstSelector.QualityParameter.ActualName = QualityParameter.Name;
#endregion
#region Create operator graph
OperatorGraph.InitialOperator = selector;
selector.Successor = subScopesProcessor1;
subScopesProcessor1.Operators.Add(new EmptyOperator());
subScopesProcessor1.Operators.Add(childrenCreator);
subScopesProcessor1.Successor = offspringSelector;
childrenCreator.Successor = uniformSubScopesProcessor;
uniformSubScopesProcessor.Operator = crossover;
uniformSubScopesProcessor.Successor = null;
crossover.Successor = osBeforeMutationBranch;
osBeforeMutationBranch.TrueBranch = evaluator1;
osBeforeMutationBranch.FalseBranch = mutationBranch2;
osBeforeMutationBranch.Successor = subScopesRemover;
evaluator1.Successor = evaluationCounter1;
evaluationCounter1.Successor = qualityComparer1;
qualityComparer1.Successor = mutationBranch1;
mutationBranch1.FirstBranch = mutator1;
mutationBranch1.SecondBranch = null;
mutationBranch1.Successor = null;
mutator1.Successor = evaluator2;
evaluator2.Successor = evaluationCounter2;
evaluationCounter2.Successor = null;
mutationBranch2.FirstBranch = mutator2;
mutationBranch2.SecondBranch = null;
mutationBranch2.Successor = evaluator3;
mutator2.Successor = null;
evaluator3.Successor = evaluationCounter3;
evaluationCounter3.Successor = qualityComparer2;
subScopesRemover.Successor = null;
offspringSelector.OffspringCreator = selector;
offspringSelector.Successor = subScopesProcessor2;
subScopesProcessor2.Operators.Add(bestSelector);
subScopesProcessor2.Operators.Add(worstSelector);
subScopesProcessor2.Successor = mergingReducer;
bestSelector.Successor = rightReducer;
rightReducer.Successor = null;
worstSelector.Successor = leftReducer;
leftReducer.Successor = null;
mergingReducer.Successor = null;
#endregion
}
public override IOperation Apply() {
if (CrossoverParameter.ActualValue == null)
return null;
return base.Apply();
}
}
}