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source: branches/HeuristicLab.DatasetRefactor/sources/HeuristicLab.Algorithms.RAPGA/3.3/RAPGA.cs @ 12105

Last change on this file since 12105 was 12105, checked in by bburlacu, 9 years ago

#2276: Merged trunk changes.

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
97      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
98    }
99    private ValueParameter<MultiAnalyzer> AnalyzerParameter {
100      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
101    }
102    private ValueParameter<IntValue> MaximumGenerationsParameter {
103      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
104    }
105    public IConstrainedValueParameter<ISolutionSimilarityCalculator> SimilarityCalculatorParameter {
106      get { return (IConstrainedValueParameter<ISolutionSimilarityCalculator>)Parameters["SimilarityCalculator"]; }
107    }
108    #endregion
109
110    #region Properties
111    public IntValue Seed {
112      get { return SeedParameter.Value; }
113      set { SeedParameter.Value = value; }
114    }
115    public BoolValue SetSeedRandomly {
116      get { return SetSeedRandomlyParameter.Value; }
117      set { SetSeedRandomlyParameter.Value = value; }
118    }
119    public IntValue PopulationSize {
120      get { return PopulationSizeParameter.Value; }
121      set { PopulationSizeParameter.Value = value; }
122    }
123    public IntValue MinimumPopulationSize {
124      get { return MinimumPopulationSizeParameter.Value; }
125      set { MinimumPopulationSizeParameter.Value = value; }
126    }
127    public IntValue MaximumPopulationSize {
128      get { return MaximumPopulationSizeParameter.Value; }
129      set { MaximumPopulationSizeParameter.Value = value; }
130    }
131    public DoubleValue ComparisonFactor {
132      get { return ComparisonFactorParameter.Value; }
133      set { ComparisonFactorParameter.Value = value; }
134    }
135    public IntValue Effort {
136      get { return EffortParameter.Value; }
137      set { EffortParameter.Value = value; }
138    }
139    public IntValue BatchSize {
140      get { return BatchSizeParameter.Value; }
141      set { BatchSizeParameter.Value = value; }
142    }
143    public ISelector Selector {
144      get { return SelectorParameter.Value; }
145      set { SelectorParameter.Value = value; }
146    }
147    public ICrossover Crossover {
148      get { return CrossoverParameter.Value; }
149      set { CrossoverParameter.Value = value; }
150    }
151    public PercentValue MutationProbability {
152      get { return MutationProbabilityParameter.Value; }
153      set { MutationProbabilityParameter.Value = value; }
154    }
155    public IManipulator Mutator {
156      get { return MutatorParameter.Value; }
157      set { MutatorParameter.Value = value; }
158    }
159    public IntValue Elites {
160      get { return ElitesParameter.Value; }
161      set { ElitesParameter.Value = value; }
162    }
163    public bool ReevaluteElites {
164      get { return ReevaluateElitesParameter.Value.Value; }
165      set { ReevaluateElitesParameter.Value.Value = value; }
166    }
167    public MultiAnalyzer Analyzer {
168      get { return AnalyzerParameter.Value; }
169      set { AnalyzerParameter.Value = value; }
170    }
171    public IntValue MaximumGenerations {
172      get { return MaximumGenerationsParameter.Value; }
173      set { MaximumGenerationsParameter.Value = value; }
174    }
175    public ISolutionSimilarityCalculator SimilarityCalculator {
176      get { return SimilarityCalculatorParameter.Value; }
177      set { SimilarityCalculatorParameter.Value = value; }
178    }
179    private RandomCreator RandomCreator {
180      get { return (RandomCreator)OperatorGraph.InitialOperator; }
181    }
182    private SolutionsCreator SolutionsCreator {
183      get { return (SolutionsCreator)RandomCreator.Successor; }
184    }
185    private RAPGAMainLoop RAPGAMainLoop {
186      get { return FindMainLoop(SolutionsCreator.Successor); }
187    }
188    [Storable]
189    private BestAverageWorstQualityAnalyzer qualityAnalyzer;
190    [Storable]
191    private PopulationSizeAnalyzer populationSizeAnalyzer;
192    [Storable]
193    private OffspringSuccessAnalyzer offspringSuccessAnalyzer;
194    [Storable]
195    private SelectionPressureAnalyzer selectionPressureAnalyzer;
196    #endregion
197
198    [StorableConstructor]
199    private RAPGA(bool deserializing) : base(deserializing) { }
200    [StorableHook(HookType.AfterDeserialization)]
201    private void AfterDeserialization() {
202      // BackwardsCompatibility3.3
203      #region Backwards compatible code, remove with 3.4
204      if (!Parameters.ContainsKey("ReevaluateElites")) {
205        Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", (BoolValue)new BoolValue(false).AsReadOnly()) { Hidden = true });
206      }
207      if (Parameters.ContainsKey("SimilarityCalculator")) {
208        var oldParameter = (IConstrainedValueParameter<ISingleObjectiveSolutionSimilarityCalculator>)Parameters["SimilarityCalculator"];
209        Parameters.Remove(oldParameter);
210        var newParameter = new ConstrainedValueParameter<ISolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions.", new ItemSet<ISolutionSimilarityCalculator>(oldParameter.ValidValues));
211        var selectedSimilarityCalculator = newParameter.ValidValues.SingleOrDefault(x => x.GetType() == oldParameter.Value.GetType());
212        newParameter.Value = selectedSimilarityCalculator;
213        Parameters.Add(newParameter);
214      }
215      #endregion
216      Initialize();
217    }
218    private RAPGA(RAPGA original, Cloner cloner)
219      : base(original, cloner) {
220      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
221      populationSizeAnalyzer = cloner.Clone(original.populationSizeAnalyzer);
222      offspringSuccessAnalyzer = cloner.Clone(original.offspringSuccessAnalyzer);
223      selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
224      Initialize();
225    }
226    public RAPGA()
227      : base() {
228      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
229      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
230      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
231      Parameters.Add(new ValueParameter<IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions.", new IntValue(2)));
232      Parameters.Add(new ValueParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions.", new IntValue(300)));
233      Parameters.Add(new ValueParameter<DoubleValue>("ComparisonFactor", "The comparison factor.", new DoubleValue(0.0)));
234      Parameters.Add(new ValueParameter<IntValue>("Effort", "The maximum number of offspring created in each generation.", new IntValue(1000)));
235      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)));
236      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
237      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
238      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
239      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
240      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
241      Parameters.Add(new FixedValueParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)", new BoolValue(false)) { Hidden = true });
242      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
243      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
244      Parameters.Add(new ConstrainedValueParameter<ISolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions."));
245
246      RandomCreator randomCreator = new RandomCreator();
247      SolutionsCreator solutionsCreator = new SolutionsCreator();
248      SubScopesCounter subScopesCounter = new SubScopesCounter();
249      ResultsCollector resultsCollector = new ResultsCollector();
250      RAPGAMainLoop mainLoop = new RAPGAMainLoop();
251      OperatorGraph.InitialOperator = randomCreator;
252
253      randomCreator.RandomParameter.ActualName = "Random";
254      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
255      randomCreator.SeedParameter.Value = null;
256      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
257      randomCreator.SetSeedRandomlyParameter.Value = null;
258      randomCreator.Successor = solutionsCreator;
259
260      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
261      solutionsCreator.Successor = subScopesCounter;
262
263      subScopesCounter.Name = "Initialize EvaluatedSolutions";
264      subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
265      subScopesCounter.Successor = resultsCollector;
266
267      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
268      resultsCollector.ResultsParameter.ActualName = "Results";
269      resultsCollector.Successor = mainLoop;
270
271      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
272      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
273      mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
274      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
275      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
276      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
277      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
278      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
279      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
280      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
281      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
282      mainLoop.ResultsParameter.ActualName = "Results";
283
284      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
285        SelectorParameter.ValidValues.Add(selector);
286      ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
287      if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
288      ParameterizeSelectors();
289
290      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
291      populationSizeAnalyzer = new PopulationSizeAnalyzer();
292      offspringSuccessAnalyzer = new OffspringSuccessAnalyzer();
293      selectionPressureAnalyzer = new SelectionPressureAnalyzer();
294      ParameterizeAnalyzers();
295      UpdateAnalyzers();
296
297      Initialize();
298    }
299    public override IDeepCloneable Clone(Cloner cloner) {
300      return new RAPGA(this, cloner);
301    }
302
303    public override void Prepare() {
304      if (Problem != null && SimilarityCalculator != null) base.Prepare();
305    }
306
307    #region Events
308    protected override void OnProblemChanged() {
309      ParameterizeStochasticOperator(Problem.SolutionCreator);
310      ParameterizeStochasticOperator(Problem.Evaluator);
311      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
312      ParameterizeSolutionsCreator();
313      ParameterizeSelectors();
314      ParameterizeAnalyzers();
315      ParameterizeIterationBasedOperators();
316      UpdateCrossovers();
317      UpdateMutators();
318      UpdateAnalyzers();
319      UpdateSimilarityCalculators();
320      ParameterizeRAPGAMainLoop();
321      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
322      base.OnProblemChanged();
323    }
324
325    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
326      ParameterizeStochasticOperator(Problem.SolutionCreator);
327      ParameterizeSolutionsCreator();
328      base.Problem_SolutionCreatorChanged(sender, e);
329    }
330    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
331      ParameterizeStochasticOperator(Problem.Evaluator);
332      ParameterizeSolutionsCreator();
333      ParameterizeRAPGAMainLoop();
334      ParameterizeSelectors();
335      ParameterizeAnalyzers();
336      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
337      base.Problem_EvaluatorChanged(sender, e);
338    }
339    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
340      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
341      ParameterizeIterationBasedOperators();
342      UpdateCrossovers();
343      UpdateMutators();
344      UpdateAnalyzers();
345      UpdateSimilarityCalculators();
346      ParameterizeRAPGAMainLoop();
347      base.Problem_OperatorsChanged(sender, e);
348    }
349    private void SimilarityCalculatorParameter_ValueChanged(object sender, EventArgs e) {
350      ParameterizeRAPGAMainLoop();
351    }
352    private void BatchSizeParameter_ValueChanged(object sender, EventArgs e) {
353      BatchSize.ValueChanged += new EventHandler(BatchSize_ValueChanged);
354      ParameterizeSelectors();
355    }
356    private void BatchSize_ValueChanged(object sender, EventArgs e) {
357      ParameterizeSelectors();
358    }
359    private void ElitesParameter_ValueChanged(object sender, EventArgs e) {
360      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
361      ParameterizeSelectors();
362    }
363    private void Elites_ValueChanged(object sender, EventArgs e) {
364      ParameterizeSelectors();
365    }
366
367    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
368      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
369      ParameterizeSelectors();
370    }
371    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
372      ParameterizeSelectors();
373    }
374    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
375      ParameterizeRAPGAMainLoop();
376      ParameterizeSelectors();
377      ParameterizeAnalyzers();
378      ParameterizeSimilarityCalculators();
379    }
380    #endregion
381
382    #region Helpers
383    private void Initialize() {
384      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
385      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
386      ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged);
387      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
388      BatchSizeParameter.ValueChanged += new EventHandler(BatchSizeParameter_ValueChanged);
389      BatchSize.ValueChanged += new EventHandler(BatchSize_ValueChanged);
390      SimilarityCalculatorParameter.ValueChanged += new EventHandler(SimilarityCalculatorParameter_ValueChanged);
391      if (Problem != null) {
392        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
393      }
394    }
395
396    private void ParameterizeSolutionsCreator() {
397      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
398      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
399    }
400    private void ParameterizeRAPGAMainLoop() {
401      RAPGAMainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
402      RAPGAMainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
403      RAPGAMainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
404    }
405    private void ParameterizeStochasticOperator(IOperator op) {
406      IStochasticOperator stochasticOp = op as IStochasticOperator;
407      if (stochasticOp != null) {
408        stochasticOp.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
409        stochasticOp.RandomParameter.Hidden = true;
410      }
411    }
412    private void ParameterizeSelectors() {
413      foreach (ISelector selector in SelectorParameter.ValidValues) {
414        selector.CopySelected = new BoolValue(true);
415        selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * BatchSize.Value);
416        selector.NumberOfSelectedSubScopesParameter.Hidden = true;
417        ParameterizeStochasticOperator(selector);
418      }
419      if (Problem != null) {
420        foreach (ISingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType<ISingleObjectiveSelector>()) {
421          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
422          selector.MaximizationParameter.Hidden = true;
423          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
424          selector.QualityParameter.Hidden = true;
425        }
426      }
427    }
428    private void ParameterizeAnalyzers() {
429      qualityAnalyzer.ResultsParameter.ActualName = "Results";
430      qualityAnalyzer.ResultsParameter.Hidden = true;
431      populationSizeAnalyzer.ResultsParameter.ActualName = "Results";
432      populationSizeAnalyzer.ResultsParameter.Hidden = true;
433      offspringSuccessAnalyzer.ResultsParameter.ActualName = "Results";
434      offspringSuccessAnalyzer.ResultsParameter.Hidden = true;
435      selectionPressureAnalyzer.ResultsParameter.ActualName = "Results";
436      selectionPressureAnalyzer.ResultsParameter.Hidden = true;
437      if (Problem != null) {
438        qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
439        qualityAnalyzer.MaximizationParameter.Hidden = true;
440        qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
441        qualityAnalyzer.QualityParameter.Depth = 1;
442        qualityAnalyzer.QualityParameter.Hidden = true;
443        qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
444        qualityAnalyzer.BestKnownQualityParameter.Hidden = true;
445      }
446    }
447    private void ParameterizeIterationBasedOperators() {
448      if (Problem != null) {
449        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
450          op.IterationsParameter.ActualName = "Generations";
451          op.IterationsParameter.Hidden = true;
452          op.MaximumIterationsParameter.ActualName = "MaximumGenerations";
453          op.MaximumIterationsParameter.Hidden = true;
454        }
455      }
456    }
457    private void ParameterizeSimilarityCalculators() {
458      foreach (ISolutionSimilarityCalculator calc in SimilarityCalculatorParameter.ValidValues) {
459        calc.QualityVariableName = Problem.Evaluator.QualityParameter.ActualName;
460      }
461    }
462    private void UpdateCrossovers() {
463      ICrossover oldCrossover = CrossoverParameter.Value;
464      CrossoverParameter.ValidValues.Clear();
465      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
466
467      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
468        CrossoverParameter.ValidValues.Add(crossover);
469
470      if (oldCrossover != null) {
471        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
472        if (crossover != null) CrossoverParameter.Value = crossover;
473        else oldCrossover = null;
474      }
475      if (oldCrossover == null && defaultCrossover != null)
476        CrossoverParameter.Value = defaultCrossover;
477    }
478    private void UpdateMutators() {
479      IManipulator oldMutator = MutatorParameter.Value;
480      MutatorParameter.ValidValues.Clear();
481      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
482        MutatorParameter.ValidValues.Add(mutator);
483      if (oldMutator != null) {
484        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
485        if (mutator != null) MutatorParameter.Value = mutator;
486      }
487    }
488    private void UpdateAnalyzers() {
489      Analyzer.Operators.Clear();
490      if (Problem != null) {
491        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
492          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
493            param.Depth = 1;
494          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
495        }
496      }
497      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
498      Analyzer.Operators.Add(populationSizeAnalyzer, populationSizeAnalyzer.EnabledByDefault);
499      Analyzer.Operators.Add(offspringSuccessAnalyzer, offspringSuccessAnalyzer.EnabledByDefault);
500      Analyzer.Operators.Add(selectionPressureAnalyzer, selectionPressureAnalyzer.EnabledByDefault);
501    }
502    private void UpdateSimilarityCalculators() {
503      ISolutionSimilarityCalculator oldSimilarityCalculator = SimilarityCalculatorParameter.Value;
504      SimilarityCalculatorParameter.ValidValues.Clear();
505      ISolutionSimilarityCalculator defaultSimilarityCalculator = Problem.Operators.OfType<ISolutionSimilarityCalculator>().FirstOrDefault();
506
507      foreach (ISolutionSimilarityCalculator similarityCalculator in Problem.Operators.OfType<ISolutionSimilarityCalculator>())
508        SimilarityCalculatorParameter.ValidValues.Add(similarityCalculator);
509
510      if (oldSimilarityCalculator != null) {
511        ISolutionSimilarityCalculator similarityCalculator = SimilarityCalculatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldSimilarityCalculator.GetType());
512        if (similarityCalculator != null) SimilarityCalculatorParameter.Value = similarityCalculator;
513        else oldSimilarityCalculator = null;
514      }
515      if (oldSimilarityCalculator == null && defaultSimilarityCalculator != null)
516        SimilarityCalculatorParameter.Value = defaultSimilarityCalculator;
517    }
518    private RAPGAMainLoop FindMainLoop(IOperator start) {
519      IOperator mainLoop = start;
520      while (mainLoop != null && !(mainLoop is RAPGAMainLoop))
521        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
522      if (mainLoop == null) return null;
523      else return (RAPGAMainLoop)mainLoop;
524    }
525    #endregion
526  }
527}
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