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source: branches/OaaS/HeuristicLab.Algorithms.RAPGA/3.3/RAPGA.cs @ 10157

Last change on this file since 10157 was 8629, checked in by jkarder, 12 years ago

#1247: added reference to algorithm description
File size: 25.6 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.RAPGA {
37  /// <summary>
38  /// A relevant alleles preserving genetic algorithm.
39  /// </summary>
40  [Item("RAPGA", "A relevant alleles preserving genetic algorithm (Affenzeller, M. et al. 2007. Self-adaptive population size adjustment for genetic algorithms. Proceedings of Computer Aided Systems Theory: EuroCAST 2007, Lecture Notes in Computer Science, pp 820–828. Springer).")]
41  [Creatable("Algorithms")]
42  [StorableClass]
43  public sealed class RAPGA : HeuristicOptimizationEngineAlgorithm, IStorableContent {
44    public string Filename { get; set; }
45
46    #region Problem Properties
47    public override Type ProblemType {
48      get { return typeof(ISingleObjectiveHeuristicOptimizationProblem); }
49    }
50    public new ISingleObjectiveHeuristicOptimizationProblem Problem {
51      get { return (ISingleObjectiveHeuristicOptimizationProblem)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    private IValueParameter<IntValue> MinimumPopulationSizeParameter {
67      get { return (IValueParameter<IntValue>)Parameters["MinimumPopulationSize"]; }
68    }
69    private IValueParameter<IntValue> MaximumPopulationSizeParameter {
70      get { return (IValueParameter<IntValue>)Parameters["MaximumPopulationSize"]; }
71    }
72    private IValueParameter<DoubleValue> ComparisonFactorParameter {
73      get { return (IValueParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
74    }
75    private IValueParameter<IntValue> EffortParameter {
76      get { return (IValueParameter<IntValue>)Parameters["Effort"]; }
77    }
78    private IValueParameter<IntValue> BatchSizeParameter {
79      get { return (IValueParameter<IntValue>)Parameters["BatchSize"]; }
80    }
81    public IConstrainedValueParameter<ISelector> SelectorParameter {
82      get { return (IConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
83    }
84    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
85      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
86    }
87    private ValueParameter<PercentValue> MutationProbabilityParameter {
88      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
89    }
90    public IConstrainedValueParameter<IManipulator> MutatorParameter {
91      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
92    }
93    private ValueParameter<IntValue> ElitesParameter {
94      get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
95    }
96    private ValueParameter<MultiAnalyzer> AnalyzerParameter {
97      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
98    }
99    private ValueParameter<IntValue> MaximumGenerationsParameter {
100      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
101    }
102    public IConstrainedValueParameter<ISingleObjectiveSolutionSimilarityCalculator> SimilarityCalculatorParameter {
103      get { return (IConstrainedValueParameter<ISingleObjectiveSolutionSimilarityCalculator>)Parameters["SimilarityCalculator"]; }
104    }
105    #endregion
106
107    #region Properties
108    public IntValue Seed {
109      get { return SeedParameter.Value; }
110      set { SeedParameter.Value = value; }
111    }
112    public BoolValue SetSeedRandomly {
113      get { return SetSeedRandomlyParameter.Value; }
114      set { SetSeedRandomlyParameter.Value = value; }
115    }
116    public IntValue PopulationSize {
117      get { return PopulationSizeParameter.Value; }
118      set { PopulationSizeParameter.Value = value; }
119    }
120    public IntValue MinimumPopulationSize {
121      get { return MinimumPopulationSizeParameter.Value; }
122      set { MinimumPopulationSizeParameter.Value = value; }
123    }
124    public IntValue MaximumPopulationSize {
125      get { return MaximumPopulationSizeParameter.Value; }
126      set { MaximumPopulationSizeParameter.Value = value; }
127    }
128    public DoubleValue ComparisonFactor {
129      get { return ComparisonFactorParameter.Value; }
130      set { ComparisonFactorParameter.Value = value; }
131    }
132    public IntValue Effort {
133      get { return EffortParameter.Value; }
134      set { EffortParameter.Value = value; }
135    }
136    public IntValue BatchSize {
137      get { return BatchSizeParameter.Value; }
138      set { BatchSizeParameter.Value = value; }
139    }
140    public ISelector Selector {
141      get { return SelectorParameter.Value; }
142      set { SelectorParameter.Value = value; }
143    }
144    public ICrossover Crossover {
145      get { return CrossoverParameter.Value; }
146      set { CrossoverParameter.Value = value; }
147    }
148    public PercentValue MutationProbability {
149      get { return MutationProbabilityParameter.Value; }
150      set { MutationProbabilityParameter.Value = value; }
151    }
152    public IManipulator Mutator {
153      get { return MutatorParameter.Value; }
154      set { MutatorParameter.Value = value; }
155    }
156    public IntValue Elites {
157      get { return ElitesParameter.Value; }
158      set { ElitesParameter.Value = value; }
159    }
160    public MultiAnalyzer Analyzer {
161      get { return AnalyzerParameter.Value; }
162      set { AnalyzerParameter.Value = value; }
163    }
164    public IntValue MaximumGenerations {
165      get { return MaximumGenerationsParameter.Value; }
166      set { MaximumGenerationsParameter.Value = value; }
167    }
168    public ISingleObjectiveSolutionSimilarityCalculator SimilarityCalculator {
169      get { return SimilarityCalculatorParameter.Value; }
170      set { SimilarityCalculatorParameter.Value = value; }
171    }
172    private RandomCreator RandomCreator {
173      get { return (RandomCreator)OperatorGraph.InitialOperator; }
174    }
175    private SolutionsCreator SolutionsCreator {
176      get { return (SolutionsCreator)RandomCreator.Successor; }
177    }
178    private RAPGAMainLoop RAPGAMainLoop {
179      get { return FindMainLoop(SolutionsCreator.Successor); }
180    }
181    [Storable]
182    private BestAverageWorstQualityAnalyzer qualityAnalyzer;
183    [Storable]
184    private PopulationSizeAnalyzer populationSizeAnalyzer;
185    [Storable]
186    private OffspringSuccessAnalyzer offspringSuccessAnalyzer;
187    [Storable]
188    private SelectionPressureAnalyzer selectionPressureAnalyzer;
189    #endregion
190
191    [StorableConstructor]
192    private RAPGA(bool deserializing) : base(deserializing) { }
193    [StorableHook(HookType.AfterDeserialization)]
194    private void AfterDeserialization() { Initialize(); }
195    private RAPGA(RAPGA original, Cloner cloner)
196      : base(original, cloner) {
197      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
198      populationSizeAnalyzer = cloner.Clone(original.populationSizeAnalyzer);
199      offspringSuccessAnalyzer = cloner.Clone(original.offspringSuccessAnalyzer);
200      selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
201      Initialize();
202    }
203    public RAPGA()
204      : base() {
205      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
206      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
207      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
208      Parameters.Add(new ValueParameter<IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions.", new IntValue(2)));
209      Parameters.Add(new ValueParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions.", new IntValue(300)));
210      Parameters.Add(new ValueParameter<DoubleValue>("ComparisonFactor", "The comparison factor.", new DoubleValue(0.0)));
211      Parameters.Add(new ValueParameter<IntValue>("Effort", "The maximum number of offspring created in each generation.", new IntValue(1000)));
212      Parameters.Add(new ValueParameter<IntValue>("BatchSize", "The number of children that should be created during one iteration of the offspring creation process.", new IntValue(10)));
213      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
214      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
215      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
216      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
217      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
218      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
219      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
220      Parameters.Add(new ConstrainedValueParameter<ISingleObjectiveSolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions."));
221
222      RandomCreator randomCreator = new RandomCreator();
223      SolutionsCreator solutionsCreator = new SolutionsCreator();
224      SubScopesCounter subScopesCounter = new SubScopesCounter();
225      ResultsCollector resultsCollector = new ResultsCollector();
226      RAPGAMainLoop mainLoop = new RAPGAMainLoop();
227      OperatorGraph.InitialOperator = randomCreator;
228
229      randomCreator.RandomParameter.ActualName = "Random";
230      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
231      randomCreator.SeedParameter.Value = null;
232      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
233      randomCreator.SetSeedRandomlyParameter.Value = null;
234      randomCreator.Successor = solutionsCreator;
235
236      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
237      solutionsCreator.Successor = subScopesCounter;
238
239      subScopesCounter.Name = "Initialize EvaluatedSolutions";
240      subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
241      subScopesCounter.Successor = resultsCollector;
242
243      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
244      resultsCollector.ResultsParameter.ActualName = "Results";
245      resultsCollector.Successor = mainLoop;
246
247      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
248      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
249      mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
250      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
251      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
252      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
253      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
254      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
255      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
256      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
257      mainLoop.ResultsParameter.ActualName = "Results";
258
259      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
260        SelectorParameter.ValidValues.Add(selector);
261      ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
262      if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
263      ParameterizeSelectors();
264
265      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
266      populationSizeAnalyzer = new PopulationSizeAnalyzer();
267      offspringSuccessAnalyzer = new OffspringSuccessAnalyzer();
268      selectionPressureAnalyzer = new SelectionPressureAnalyzer();
269      ParameterizeAnalyzers();
270      UpdateAnalyzers();
271
272      Initialize();
273    }
274    public override IDeepCloneable Clone(Cloner cloner) {
275      return new RAPGA(this, cloner);
276    }
277
278    public override void Prepare() {
279      if (Problem != null && SimilarityCalculator != null) base.Prepare();
280    }
281
282    #region Events
283    protected override void OnProblemChanged() {
284      ParameterizeStochasticOperator(Problem.SolutionCreator);
285      ParameterizeStochasticOperator(Problem.Evaluator);
286      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
287      ParameterizeSolutionsCreator();
288      ParameterizeSelectors();
289      ParameterizeAnalyzers();
290      ParameterizeIterationBasedOperators();
291      UpdateCrossovers();
292      UpdateMutators();
293      UpdateAnalyzers();
294      UpdateSimilarityCalculators();
295      ParameterizeRAPGAMainLoop();
296      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
297      base.OnProblemChanged();
298    }
299
300    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
301      ParameterizeStochasticOperator(Problem.SolutionCreator);
302      ParameterizeSolutionsCreator();
303      base.Problem_SolutionCreatorChanged(sender, e);
304    }
305    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
306      ParameterizeStochasticOperator(Problem.Evaluator);
307      ParameterizeSolutionsCreator();
308      ParameterizeRAPGAMainLoop();
309      ParameterizeSelectors();
310      ParameterizeAnalyzers();
311      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
312      base.Problem_EvaluatorChanged(sender, e);
313    }
314    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
315      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
316      ParameterizeIterationBasedOperators();
317      UpdateCrossovers();
318      UpdateMutators();
319      UpdateAnalyzers();
320      UpdateSimilarityCalculators();
321      ParameterizeRAPGAMainLoop();
322      base.Problem_OperatorsChanged(sender, e);
323    }
324    private void SimilarityCalculatorParameter_ValueChanged(object sender, EventArgs e) {
325      ParameterizeRAPGAMainLoop();
326    }
327    private void BatchSizeParameter_ValueChanged(object sender, EventArgs e) {
328      BatchSize.ValueChanged += new EventHandler(BatchSize_ValueChanged);
329      ParameterizeSelectors();
330    }
331    private void BatchSize_ValueChanged(object sender, EventArgs e) {
332      ParameterizeSelectors();
333    }
334    private void ElitesParameter_ValueChanged(object sender, EventArgs e) {
335      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
336      ParameterizeSelectors();
337    }
338    private void Elites_ValueChanged(object sender, EventArgs e) {
339      ParameterizeSelectors();
340    }
341
342    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
343      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
344      ParameterizeSelectors();
345    }
346    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
347      ParameterizeSelectors();
348    }
349    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
350      ParameterizeRAPGAMainLoop();
351      ParameterizeSelectors();
352      ParameterizeAnalyzers();
353      ParameterizeSimilarityCalculators();
354    }
355    #endregion
356
357    #region Helpers
358    private void Initialize() {
359      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
360      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
361      ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged);
362      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
363      BatchSizeParameter.ValueChanged += new EventHandler(BatchSizeParameter_ValueChanged);
364      BatchSize.ValueChanged += new EventHandler(BatchSize_ValueChanged);
365      SimilarityCalculatorParameter.ValueChanged += new EventHandler(SimilarityCalculatorParameter_ValueChanged);
366      if (Problem != null) {
367        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
368      }
369    }
370
371    private void ParameterizeSolutionsCreator() {
372      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
373      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
374    }
375    private void ParameterizeRAPGAMainLoop() {
376      RAPGAMainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
377      RAPGAMainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
378      RAPGAMainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
379      foreach (ISimilarityBasedOperator op in RAPGAMainLoop.OperatorGraph.Operators.OfType<ISimilarityBasedOperator>())
380        op.SimilarityCalculator = SimilarityCalculator;
381    }
382    private void ParameterizeStochasticOperator(IOperator op) {
383      IStochasticOperator stochasticOp = op as IStochasticOperator;
384      if (stochasticOp != null) {
385        stochasticOp.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
386        stochasticOp.RandomParameter.Hidden = true;
387      }
388    }
389    private void ParameterizeSelectors() {
390      foreach (ISelector selector in SelectorParameter.ValidValues) {
391        selector.CopySelected = new BoolValue(true);
392        selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * BatchSize.Value);
393        selector.NumberOfSelectedSubScopesParameter.Hidden = true;
394        ParameterizeStochasticOperator(selector);
395      }
396      if (Problem != null) {
397        foreach (ISingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType<ISingleObjectiveSelector>()) {
398          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
399          selector.MaximizationParameter.Hidden = true;
400          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
401          selector.QualityParameter.Hidden = true;
402        }
403      }
404    }
405    private void ParameterizeAnalyzers() {
406      qualityAnalyzer.ResultsParameter.ActualName = "Results";
407      qualityAnalyzer.ResultsParameter.Hidden = true;
408      populationSizeAnalyzer.ResultsParameter.ActualName = "Results";
409      populationSizeAnalyzer.ResultsParameter.Hidden = true;
410      offspringSuccessAnalyzer.ResultsParameter.ActualName = "Results";
411      offspringSuccessAnalyzer.ResultsParameter.Hidden = true;
412      selectionPressureAnalyzer.ResultsParameter.ActualName = "Results";
413      selectionPressureAnalyzer.ResultsParameter.Hidden = true;
414      if (Problem != null) {
415        qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
416        qualityAnalyzer.MaximizationParameter.Hidden = true;
417        qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
418        qualityAnalyzer.QualityParameter.Depth = 1;
419        qualityAnalyzer.QualityParameter.Hidden = true;
420        qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
421        qualityAnalyzer.BestKnownQualityParameter.Hidden = true;
422      }
423    }
424    private void ParameterizeIterationBasedOperators() {
425      if (Problem != null) {
426        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
427          op.IterationsParameter.ActualName = "Generations";
428          op.IterationsParameter.Hidden = true;
429          op.MaximumIterationsParameter.ActualName = "MaximumGenerations";
430          op.MaximumIterationsParameter.Hidden = true;
431        }
432      }
433    }
434    private void ParameterizeSimilarityCalculators() {
435      foreach (ISingleObjectiveSolutionSimilarityCalculator calc in SimilarityCalculatorParameter.ValidValues) {
436        calc.QualityVariableName = Problem.Evaluator.QualityParameter.ActualName;
437      }
438    }
439    private void UpdateCrossovers() {
440      ICrossover oldCrossover = CrossoverParameter.Value;
441      CrossoverParameter.ValidValues.Clear();
442      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
443
444      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
445        CrossoverParameter.ValidValues.Add(crossover);
446
447      if (oldCrossover != null) {
448        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
449        if (crossover != null) CrossoverParameter.Value = crossover;
450        else oldCrossover = null;
451      }
452      if (oldCrossover == null && defaultCrossover != null)
453        CrossoverParameter.Value = defaultCrossover;
454    }
455    private void UpdateMutators() {
456      IManipulator oldMutator = MutatorParameter.Value;
457      MutatorParameter.ValidValues.Clear();
458      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
459        MutatorParameter.ValidValues.Add(mutator);
460      if (oldMutator != null) {
461        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
462        if (mutator != null) MutatorParameter.Value = mutator;
463      }
464    }
465    private void UpdateAnalyzers() {
466      Analyzer.Operators.Clear();
467      if (Problem != null) {
468        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
469          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
470            param.Depth = 1;
471          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
472        }
473      }
474      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
475      Analyzer.Operators.Add(populationSizeAnalyzer, populationSizeAnalyzer.EnabledByDefault);
476      Analyzer.Operators.Add(offspringSuccessAnalyzer, offspringSuccessAnalyzer.EnabledByDefault);
477      Analyzer.Operators.Add(selectionPressureAnalyzer, selectionPressureAnalyzer.EnabledByDefault);
478    }
479    private void UpdateSimilarityCalculators() {
480      ISingleObjectiveSolutionSimilarityCalculator oldSimilarityCalculator = SimilarityCalculatorParameter.Value;
481      SimilarityCalculatorParameter.ValidValues.Clear();
482      ISingleObjectiveSolutionSimilarityCalculator defaultSimilarityCalculator = Problem.Operators.OfType<ISingleObjectiveSolutionSimilarityCalculator>().FirstOrDefault();
483
484      SimilarityCalculatorParameter.ValidValues.Add(new QualitySimilarityCalculator { QualityVariableName = Problem.Evaluator.QualityParameter.ActualName });
485      SimilarityCalculatorParameter.ValidValues.Add(new NoSimilarityCalculator());
486
487      foreach (ISingleObjectiveSolutionSimilarityCalculator similarityCalculator in Problem.Operators.OfType<ISingleObjectiveSolutionSimilarityCalculator>())
488        SimilarityCalculatorParameter.ValidValues.Add(similarityCalculator);
489
490      if (oldSimilarityCalculator != null) {
491        ISingleObjectiveSolutionSimilarityCalculator similarityCalculator = SimilarityCalculatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldSimilarityCalculator.GetType());
492        if (similarityCalculator != null) SimilarityCalculatorParameter.Value = similarityCalculator;
493        else oldSimilarityCalculator = null;
494      }
495      if (oldSimilarityCalculator == null && defaultSimilarityCalculator != null)
496        SimilarityCalculatorParameter.Value = defaultSimilarityCalculator;
497    }
498    private RAPGAMainLoop FindMainLoop(IOperator start) {
499      IOperator mainLoop = start;
500      while (mainLoop != null && !(mainLoop is RAPGAMainLoop))
501        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
502      if (mainLoop == null) return null;
503      else return (RAPGAMainLoop)mainLoop;
504    }
505    #endregion
506  }
507}
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