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source: branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2MainLoop.cs @ 12214

Last change on this file since 12214 was 11185, checked in by pfleck, 10 years ago

#2208 merged trunk and updated version info
File size: 11.8 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Operators;
26using HeuristicLab.Optimization.Operators;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Selection;
30
31namespace HeuristicLab.Algorithms.NSGA2 {
32  /// <summary>
33  /// An operator that represents the mainloop of the NSGA-II
34  /// </summary>
35  [Item("NSGA2MainLoop", "An operator which represents the main loop of the NSGA-II algorithm.")]
36  [StorableClass]
37  public class NSGA2MainLoop : AlgorithmOperator {
38    #region Parameter properties
39    public ValueLookupParameter<IRandom> RandomParameter {
40      get { return (ValueLookupParameter<IRandom>)Parameters["Random"]; }
41    }
42    public ValueLookupParameter<BoolArray> MaximizationParameter {
43      get { return (ValueLookupParameter<BoolArray>)Parameters["Maximization"]; }
44    }
45    public ScopeTreeLookupParameter<DoubleArray> QualitiesParameter {
46      get { return (ScopeTreeLookupParameter<DoubleArray>)Parameters["Qualities"]; }
47    }
48    public ValueLookupParameter<IntValue> PopulationSizeParameter {
49      get { return (ValueLookupParameter<IntValue>)Parameters["PopulationSize"]; }
50    }
51    public ValueLookupParameter<IOperator> SelectorParameter {
52      get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
53    }
54    public ValueLookupParameter<PercentValue> CrossoverProbabilityParameter {
55      get { return (ValueLookupParameter<PercentValue>)Parameters["CrossoverProbability"]; }
56    }
57    public ValueLookupParameter<IOperator> CrossoverParameter {
58      get { return (ValueLookupParameter<IOperator>)Parameters["Crossover"]; }
59    }
60    public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
61      get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
62    }
63    public ValueLookupParameter<IOperator> MutatorParameter {
64      get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
65    }
66    public ValueLookupParameter<IOperator> EvaluatorParameter {
67      get { return (ValueLookupParameter<IOperator>)Parameters["Evaluator"]; }
68    }
69    public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
70      get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
71    }
72    public ValueLookupParameter<VariableCollection> ResultsParameter {
73      get { return (ValueLookupParameter<VariableCollection>)Parameters["Results"]; }
74    }
75    public ValueLookupParameter<IOperator> AnalyzerParameter {
76      get { return (ValueLookupParameter<IOperator>)Parameters["Analyzer"]; }
77    }
78    public LookupParameter<IntValue> EvaluatedSolutionsParameter {
79      get { return (LookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
80    }
81    #endregion
82
83    [StorableConstructor]
84    protected NSGA2MainLoop(bool deserializing) : base(deserializing) { }
85    protected NSGA2MainLoop(NSGA2MainLoop original, Cloner cloner) : base(original, cloner) { }
86    public NSGA2MainLoop()
87      : base() {
88      Initialize();
89    }
90
91    private void Initialize() {
92      #region Create parameters
93      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
94      Parameters.Add(new ValueLookupParameter<BoolArray>("Maximization", "True if an objective should be maximized, or false if it should be minimized."));
95      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>("Qualities", "The vector of quality values."));
96      Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The population size."));
97      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
98      Parameters.Add(new ValueLookupParameter<PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on a solution."));
99      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
100      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
101      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
102      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."));
103      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
104      Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
105      Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
106      Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
107      #endregion
108
109      #region Create operators
110      VariableCreator variableCreator = new VariableCreator();
111      ResultsCollector resultsCollector1 = new ResultsCollector();
112      Placeholder analyzer1 = new Placeholder();
113      Placeholder selector = new Placeholder();
114      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
115      ChildrenCreator childrenCreator = new ChildrenCreator();
116      UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
117      StochasticBranch crossoverStochasticBranch = new StochasticBranch();
118      Placeholder crossover = new Placeholder();
119      ParentCopyCrossover noCrossover = new ParentCopyCrossover();
120      StochasticBranch mutationStochasticBranch = new StochasticBranch();
121      Placeholder mutator = new Placeholder();
122      SubScopesRemover subScopesRemover = new SubScopesRemover();
123      UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
124      Placeholder evaluator = new Placeholder();
125      SubScopesCounter subScopesCounter = new SubScopesCounter();
126      MergingReducer mergingReducer = new MergingReducer();
127      RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
128      LeftSelector leftSelector = new LeftSelector();
129      RightReducer rightReducer = new RightReducer();
130      IntCounter intCounter = new IntCounter();
131      Comparator comparator = new Comparator();
132      Placeholder analyzer2 = new Placeholder();
133      ConditionalBranch conditionalBranch = new ConditionalBranch();
134
135      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0)));
136
137      resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
138      resultsCollector1.ResultsParameter.ActualName = ResultsParameter.Name;
139
140      analyzer1.Name = "Analyzer";
141      analyzer1.OperatorParameter.ActualName = AnalyzerParameter.Name;
142
143      selector.Name = "Selector";
144      selector.OperatorParameter.ActualName = SelectorParameter.Name;
145
146      childrenCreator.ParentsPerChild = new IntValue(2);
147
148      crossoverStochasticBranch.ProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
149      crossoverStochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
150
151      crossover.Name = "Crossover";
152      crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
153
154      noCrossover.Name = "Clone parent";
155      noCrossover.RandomParameter.ActualName = RandomParameter.Name;
156
157      mutationStochasticBranch.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
158      mutationStochasticBranch.RandomParameter.ActualName = RandomParameter.Name;
159
160      mutator.Name = "Mutator";
161      mutator.OperatorParameter.ActualName = MutatorParameter.Name;
162
163      subScopesRemover.RemoveAllSubScopes = true;
164
165      uniformSubScopesProcessor2.Parallel.Value = true;
166
167      evaluator.Name = "Evaluator";
168      evaluator.OperatorParameter.ActualName = EvaluatorParameter.Name;
169
170      subScopesCounter.Name = "Increment EvaluatedSolutions";
171      subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
172
173      rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
174      rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
175
176      leftSelector.CopySelected = new BoolValue(false);
177      leftSelector.NumberOfSelectedSubScopesParameter.ActualName = PopulationSizeParameter.Name;
178
179      intCounter.Increment = new IntValue(1);
180      intCounter.ValueParameter.ActualName = "Generations";
181
182      comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
183      comparator.LeftSideParameter.ActualName = "Generations";
184      comparator.ResultParameter.ActualName = "Terminate";
185      comparator.RightSideParameter.ActualName = MaximumGenerationsParameter.Name;
186
187      analyzer2.Name = "Analyzer";
188      analyzer2.OperatorParameter.ActualName = "Analyzer";
189
190      conditionalBranch.ConditionParameter.ActualName = "Terminate";
191      #endregion
192
193      #region Create operator graph
194      OperatorGraph.InitialOperator = variableCreator;
195      variableCreator.Successor = resultsCollector1;
196      resultsCollector1.Successor = analyzer1;
197      analyzer1.Successor = selector;
198      selector.Successor = subScopesProcessor1;
199      subScopesProcessor1.Operators.Add(new EmptyOperator());
200      subScopesProcessor1.Operators.Add(childrenCreator);
201      subScopesProcessor1.Successor = mergingReducer;
202      childrenCreator.Successor = uniformSubScopesProcessor1;
203      uniformSubScopesProcessor1.Operator = crossoverStochasticBranch;
204      uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
205      crossoverStochasticBranch.FirstBranch = crossover;
206      crossoverStochasticBranch.SecondBranch = noCrossover;
207      crossoverStochasticBranch.Successor = mutationStochasticBranch;
208      crossover.Successor = null;
209      noCrossover.Successor = null;
210      mutationStochasticBranch.FirstBranch = mutator;
211      mutationStochasticBranch.SecondBranch = null;
212      mutationStochasticBranch.Successor = subScopesRemover;
213      mutator.Successor = null;
214      subScopesRemover.Successor = null;
215      uniformSubScopesProcessor2.Operator = evaluator;
216      uniformSubScopesProcessor2.Successor = subScopesCounter;
217      evaluator.Successor = null;
218      subScopesCounter.Successor = null;
219      mergingReducer.Successor = rankAndCrowdingSorter;
220      rankAndCrowdingSorter.Successor = leftSelector;
221      leftSelector.Successor = rightReducer;
222      rightReducer.Successor = intCounter;
223      intCounter.Successor = comparator;
224      comparator.Successor = analyzer2;
225      analyzer2.Successor = conditionalBranch;
226      conditionalBranch.FalseBranch = selector;
227      conditionalBranch.TrueBranch = null;
228      conditionalBranch.Successor = null;
229      #endregion
230    }
231
232    public override IDeepCloneable Clone(Cloner cloner) {
233      return new NSGA2MainLoop(this, cloner);
234    }
235  }
236}
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