source: trunk/sources/HeuristicLab.Algorithms.NSGA2/3.3/NSGA2.cs @ 15583

Last change on this file since 15583 was 15583, checked in by swagner, 22 months ago

#2640: Updated year of copyrights in license headers

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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 System;
23using System.Linq;
24using HeuristicLab.Analysis;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Operators;
29using HeuristicLab.Optimization;
30using HeuristicLab.Optimization.Operators;
31using HeuristicLab.Parameters;
32using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
33using HeuristicLab.PluginInfrastructure;
34using HeuristicLab.Random;
35
36namespace HeuristicLab.Algorithms.NSGA2 {
37  /// <summary>
38  /// The Nondominated Sorting Genetic Algorithm II was introduced in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.
39  /// </summary>
40  [Item("NSGA-II", "The Nondominated Sorting Genetic Algorithm II was introduced in Deb et al. 2002. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), pp. 182-197.")]
41  [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 135)]
42  [StorableClass]
43  public class NSGA2 : HeuristicOptimizationEngineAlgorithm, IStorableContent {
44    public string Filename { get; set; }
45
46    #region Problem Properties
47    public override Type ProblemType {
48      get { return typeof(IMultiObjectiveHeuristicOptimizationProblem); }
49    }
50    public new IMultiObjectiveHeuristicOptimizationProblem Problem {
51      get { return (IMultiObjectiveHeuristicOptimizationProblem)base.Problem; }
52      set { base.Problem = value; }
53    }
54    #endregion
55
56    #region Parameter Properties
57    private ValueParameter<IntValue> SeedParameter {
58      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
59    }
60    private ValueParameter<BoolValue> SetSeedRandomlyParameter {
61      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
62    }
63    private ValueParameter<IntValue> PopulationSizeParameter {
64      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }
65    }
66    public IConstrainedValueParameter<ISelector> SelectorParameter {
67      get { return (IConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
68    }
69    private ValueParameter<PercentValue> CrossoverProbabilityParameter {
70      get { return (ValueParameter<PercentValue>)Parameters["CrossoverProbability"]; }
71    }
72    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
73      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
74    }
75    private ValueParameter<PercentValue> MutationProbabilityParameter {
76      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
77    }
78    public IConstrainedValueParameter<IManipulator> MutatorParameter {
79      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
80    }
81    private ValueParameter<MultiAnalyzer> AnalyzerParameter {
82      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
83    }
84    private ValueParameter<IntValue> MaximumGenerationsParameter {
85      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
86    }
87    private ValueParameter<IntValue> SelectedParentsParameter {
88      get { return (ValueParameter<IntValue>)Parameters["SelectedParents"]; }
89    }
90
91    private IFixedValueParameter<BoolValue> DominateOnEqualQualitiesParameter {
92      get { return (IFixedValueParameter<BoolValue>)Parameters["DominateOnEqualQualities"]; }
93    }
94    #endregion
95
96    #region Properties
97    public IntValue Seed {
98      get { return SeedParameter.Value; }
99      set { SeedParameter.Value = value; }
100    }
101    public BoolValue SetSeedRandomly {
102      get { return SetSeedRandomlyParameter.Value; }
103      set { SetSeedRandomlyParameter.Value = value; }
104    }
105    public IntValue PopulationSize {
106      get { return PopulationSizeParameter.Value; }
107      set { PopulationSizeParameter.Value = value; }
108    }
109    public ISelector Selector {
110      get { return SelectorParameter.Value; }
111      set { SelectorParameter.Value = value; }
112    }
113    public PercentValue CrossoverProbability {
114      get { return CrossoverProbabilityParameter.Value; }
115      set { CrossoverProbabilityParameter.Value = value; }
116    }
117    public ICrossover Crossover {
118      get { return CrossoverParameter.Value; }
119      set { CrossoverParameter.Value = value; }
120    }
121    public PercentValue MutationProbability {
122      get { return MutationProbabilityParameter.Value; }
123      set { MutationProbabilityParameter.Value = value; }
124    }
125    public IManipulator Mutator {
126      get { return MutatorParameter.Value; }
127      set { MutatorParameter.Value = value; }
128    }
129    public MultiAnalyzer Analyzer {
130      get { return AnalyzerParameter.Value; }
131      set { AnalyzerParameter.Value = value; }
132    }
133    public IntValue MaximumGenerations {
134      get { return MaximumGenerationsParameter.Value; }
135      set { MaximumGenerationsParameter.Value = value; }
136    }
137    public IntValue SelectedParents {
138      get { return SelectedParentsParameter.Value; }
139      set { SelectedParentsParameter.Value = value; }
140    }
141    public bool DominateOnEqualQualities {
142      get { return DominateOnEqualQualitiesParameter.Value.Value; }
143      set { DominateOnEqualQualitiesParameter.Value.Value = value; }
144    }
145
146    private RandomCreator RandomCreator {
147      get { return (RandomCreator)OperatorGraph.InitialOperator; }
148    }
149    private SolutionsCreator SolutionsCreator {
150      get { return (SolutionsCreator)RandomCreator.Successor; }
151    }
152    private RankAndCrowdingSorter RankAndCrowdingSorter {
153      get { return (RankAndCrowdingSorter)((SubScopesCounter)SolutionsCreator.Successor).Successor; }
154    }
155    private NSGA2MainLoop MainLoop {
156      get { return FindMainLoop(RankAndCrowdingSorter.Successor); }
157    }
158    #endregion
159
160    [Storable]
161    private RankBasedParetoFrontAnalyzer paretoFrontAnalyzer;
162
163    [StorableConstructor]
164    protected NSGA2(bool deserializing) : base(deserializing) { }
165    protected NSGA2(NSGA2 original, Cloner cloner)
166      : base(original, cloner) {
167      paretoFrontAnalyzer = (RankBasedParetoFrontAnalyzer)cloner.Clone(original.paretoFrontAnalyzer);
168      AfterDeserialization();
169    }
170    public NSGA2() {
171      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
172      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
173      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
174      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
175      Parameters.Add(new ValueParameter<PercentValue>("CrossoverProbability", "The probability that the crossover operator is applied on two parents.", new PercentValue(0.9)));
176      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
177      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
178      Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
179      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
180      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
181      Parameters.Add(new ValueParameter<IntValue>("SelectedParents", "Each two parents form a new child, typically this value should be twice the population size, but because the NSGA-II is maximally elitist it can be any multiple of 2 greater than 0.", new IntValue(200)));
182      Parameters.Add(new FixedValueParameter<BoolValue>("DominateOnEqualQualities", "Flag which determines wether solutions with equal quality values should be treated as dominated.", new BoolValue(false)));
183
184      RandomCreator randomCreator = new RandomCreator();
185      SolutionsCreator solutionsCreator = new SolutionsCreator();
186      SubScopesCounter subScopesCounter = new SubScopesCounter();
187      RankAndCrowdingSorter rankAndCrowdingSorter = new RankAndCrowdingSorter();
188      ResultsCollector resultsCollector = new ResultsCollector();
189      NSGA2MainLoop mainLoop = new NSGA2MainLoop();
190
191      OperatorGraph.InitialOperator = randomCreator;
192
193      randomCreator.RandomParameter.ActualName = "Random";
194      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
195      randomCreator.SeedParameter.Value = null;
196      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
197      randomCreator.SetSeedRandomlyParameter.Value = null;
198      randomCreator.Successor = solutionsCreator;
199
200      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
201      solutionsCreator.Successor = subScopesCounter;
202
203      subScopesCounter.Name = "Initialize EvaluatedSolutions";
204      subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
205      subScopesCounter.Successor = rankAndCrowdingSorter;
206
207      rankAndCrowdingSorter.DominateOnEqualQualitiesParameter.ActualName = DominateOnEqualQualitiesParameter.Name;
208      rankAndCrowdingSorter.CrowdingDistanceParameter.ActualName = "CrowdingDistance";
209      rankAndCrowdingSorter.RankParameter.ActualName = "Rank";
210      rankAndCrowdingSorter.Successor = resultsCollector;
211
212      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
213      resultsCollector.ResultsParameter.ActualName = "Results";
214      resultsCollector.Successor = mainLoop;
215
216      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
217      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
218      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
219      mainLoop.CrossoverProbabilityParameter.ActualName = CrossoverProbabilityParameter.Name;
220      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
221      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
222      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
223      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
224      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
225      mainLoop.ResultsParameter.ActualName = "Results";
226      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
227
228      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is ISingleObjectiveSelector)).OrderBy(x => x.Name))
229        SelectorParameter.ValidValues.Add(selector);
230      ISelector tournamentSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("CrowdedTournamentSelector"));
231      if (tournamentSelector != null) SelectorParameter.Value = tournamentSelector;
232
233      ParameterizeSelectors();
234
235      paretoFrontAnalyzer = new RankBasedParetoFrontAnalyzer();
236      paretoFrontAnalyzer.RankParameter.ActualName = "Rank";
237      paretoFrontAnalyzer.RankParameter.Depth = 1;
238      paretoFrontAnalyzer.ResultsParameter.ActualName = "Results";
239      ParameterizeAnalyzers();
240      UpdateAnalyzers();
241
242      AfterDeserialization();
243    }
244
245    public override IDeepCloneable Clone(Cloner cloner) {
246      return new NSGA2(this, cloner);
247    }
248
249    public override void Prepare() {
250      if (Problem != null) base.Prepare();
251    }
252
253    #region Events
254    protected override void OnProblemChanged() {
255      ParameterizeStochasticOperator(Problem.SolutionCreator);
256      ParameterizeStochasticOperator(Problem.Evaluator);
257      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
258      ParameterizeSolutionsCreator();
259      ParameterizeRankAndCrowdingSorter();
260      ParameterizeMainLoop();
261      ParameterizeSelectors();
262      ParameterizeAnalyzers();
263      ParameterizeIterationBasedOperators();
264      UpdateCrossovers();
265      UpdateMutators();
266      UpdateAnalyzers();
267      Problem.Evaluator.QualitiesParameter.ActualNameChanged += new EventHandler(Evaluator_QualitiesParameter_ActualNameChanged);
268      base.OnProblemChanged();
269    }
270    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
271      ParameterizeStochasticOperator(Problem.SolutionCreator);
272      ParameterizeSolutionsCreator();
273      base.Problem_SolutionCreatorChanged(sender, e);
274    }
275    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
276      ParameterizeStochasticOperator(Problem.Evaluator);
277      ParameterizeSolutionsCreator();
278      ParameterizeRankAndCrowdingSorter();
279      ParameterizeMainLoop();
280      ParameterizeSelectors();
281      ParameterizeAnalyzers();
282      Problem.Evaluator.QualitiesParameter.ActualNameChanged += new EventHandler(Evaluator_QualitiesParameter_ActualNameChanged);
283      base.Problem_EvaluatorChanged(sender, e);
284    }
285    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
286      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
287      ParameterizeIterationBasedOperators();
288      UpdateCrossovers();
289      UpdateMutators();
290      UpdateAnalyzers();
291      base.Problem_OperatorsChanged(sender, e);
292    }
293    protected override void Problem_Reset(object sender, EventArgs e) {
294      base.Problem_Reset(sender, e);
295    }
296    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
297      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
298      ParameterizeSelectors();
299    }
300    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
301      ParameterizeSelectors();
302    }
303    private void Evaluator_QualitiesParameter_ActualNameChanged(object sender, EventArgs e) {
304      ParameterizeRankAndCrowdingSorter();
305      ParameterizeMainLoop();
306      ParameterizeSelectors();
307      ParameterizeAnalyzers();
308    }
309    private void SelectedParentsParameter_ValueChanged(object sender, EventArgs e) {
310      SelectedParents.ValueChanged += new EventHandler(SelectedParents_ValueChanged);
311      SelectedParents_ValueChanged(null, EventArgs.Empty);
312    }
313    private void SelectedParents_ValueChanged(object sender, EventArgs e) {
314      if (SelectedParents.Value < 2) SelectedParents.Value = 2;
315      else if (SelectedParents.Value % 2 != 0) {
316        SelectedParents.Value = SelectedParents.Value + 1;
317      }
318    }
319    #endregion
320
321    #region Helpers
322    [StorableHook(HookType.AfterDeserialization)]
323    private void AfterDeserialization() {
324      // BackwardsCompatibility3.3
325      #region Backwards compatible code, remove with 3.4
326      if (!Parameters.ContainsKey("DominateOnEqualQualities"))
327        Parameters.Add(new FixedValueParameter<BoolValue>("DominateOnEqualQualities", "Flag which determines wether solutions with equal quality values should be treated as dominated.", new BoolValue(false)));
328      var optionalMutatorParameter = MutatorParameter as OptionalConstrainedValueParameter<IManipulator>;
329      if (optionalMutatorParameter != null) {
330        Parameters.Remove(optionalMutatorParameter);
331        Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
332        foreach (var m in optionalMutatorParameter.ValidValues)
333          MutatorParameter.ValidValues.Add(m);
334        if (optionalMutatorParameter.Value == null) MutationProbability.Value = 0; // to guarantee that the old configuration results in the same behavior
335        else Mutator = optionalMutatorParameter.Value;
336        optionalMutatorParameter.ValidValues.Clear(); // to avoid dangling references to the old parameter its valid values are cleared
337      }
338      #endregion
339
340      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
341      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
342      SelectedParentsParameter.ValueChanged += new EventHandler(SelectedParentsParameter_ValueChanged);
343      SelectedParents.ValueChanged += new EventHandler(SelectedParents_ValueChanged);
344      if (Problem != null) {
345        Problem.Evaluator.QualitiesParameter.ActualNameChanged += new EventHandler(Evaluator_QualitiesParameter_ActualNameChanged);
346      }
347    }
348    private void ParameterizeSolutionsCreator() {
349      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
350      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
351    }
352    private void ParameterizeRankAndCrowdingSorter() {
353      RankAndCrowdingSorter.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
354      RankAndCrowdingSorter.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
355    }
356    private void ParameterizeMainLoop() {
357      MainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
358      MainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
359      MainLoop.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
360    }
361    private void ParameterizeStochasticOperator(IOperator op) {
362      if (op is IStochasticOperator)
363        ((IStochasticOperator)op).RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
364    }
365    private void ParameterizeSelectors() {
366      foreach (ISelector selector in SelectorParameter.ValidValues) {
367        selector.CopySelected = new BoolValue(true);
368        selector.NumberOfSelectedSubScopesParameter.ActualName = SelectedParentsParameter.Name;
369        ParameterizeStochasticOperator(selector);
370      }
371      if (Problem != null) {
372        foreach (IMultiObjectiveSelector selector in SelectorParameter.ValidValues.OfType<IMultiObjectiveSelector>()) {
373          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
374          selector.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
375        }
376      }
377    }
378    private void ParameterizeAnalyzers() {
379      if (Problem != null) {
380        paretoFrontAnalyzer.QualitiesParameter.ActualName = Problem.Evaluator.QualitiesParameter.ActualName;
381        paretoFrontAnalyzer.QualitiesParameter.Depth = 1;
382      }
383    }
384    private void ParameterizeIterationBasedOperators() {
385      if (Problem != null) {
386        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
387          op.IterationsParameter.ActualName = "Generations";
388          op.MaximumIterationsParameter.ActualName = "MaximumGenerations";
389        }
390      }
391    }
392    private void UpdateCrossovers() {
393      ICrossover oldCrossover = CrossoverParameter.Value;
394      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
395      CrossoverParameter.ValidValues.Clear();
396      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
397        CrossoverParameter.ValidValues.Add(crossover);
398      if (oldCrossover != null) {
399        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
400        if (crossover != null) CrossoverParameter.Value = crossover;
401        else oldCrossover = null;
402      }
403      if (oldCrossover == null && defaultCrossover != null)
404        CrossoverParameter.Value = defaultCrossover;
405    }
406    private void UpdateMutators() {
407      IManipulator oldMutator = MutatorParameter.Value;
408      MutatorParameter.ValidValues.Clear();
409      IManipulator defaultMutator = Problem.Operators.OfType<IManipulator>().FirstOrDefault();
410      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
411        MutatorParameter.ValidValues.Add(mutator);
412      if (oldMutator != null) {
413        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
414        if (mutator != null) MutatorParameter.Value = mutator;
415        else oldMutator = null;
416      }
417      if (oldMutator == null && defaultMutator != null)
418        MutatorParameter.Value = defaultMutator;
419    }
420    private void UpdateAnalyzers() {
421      Analyzer.Operators.Clear();
422      if (Problem != null) {
423        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
424          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
425            param.Depth = 1;
426          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
427        }
428      }
429      Analyzer.Operators.Add(paretoFrontAnalyzer, paretoFrontAnalyzer.EnabledByDefault);
430    }
431    private NSGA2MainLoop FindMainLoop(IOperator start) {
432      IOperator mainLoop = start;
433      while (mainLoop != null && !(mainLoop is NSGA2MainLoop))
434        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
435      if (mainLoop == null) return null;
436      else return (NSGA2MainLoop)mainLoop;
437    }
438    #endregion
439  }
440}
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