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source: trunk/sources/HeuristicLab.Algorithms.RAPGA/3.3/RAPGA.cs @ 12099

Last change on this file since 12099 was 12069, checked in by jkarder, 10 years ago

#2332: refactored operators and analyzers

  • removed quality and maximization parameters in SingleObjectivePopulationDiversityAnalyzer
  • renamed SingleObjectivePopulationDiversityAnalyzer to PopulationSimilarityAnalyzer
  • added ConstrainedValueParameter for similarity calculators of analyzer
  • added ValueLookupParameter for similarity calculator of the following operators:
    • DuplicatesSelector, ProgressiveOffspringPreserver, ReferenceSetUpdateMethod, SolutionPoolUpdateMethod
  • removed some wiring code in specific problems
File size: 26.5 KB
Line 
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<ISingleObjectiveSolutionSimilarityCalculator> SimilarityCalculatorParameter {
106      get { return (IConstrainedValueParameter<ISingleObjectiveSolutionSimilarityCalculator>)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 ISingleObjectiveSolutionSimilarityCalculator 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      #endregion
208      Initialize();
209    }
210    private RAPGA(RAPGA original, Cloner cloner)
211      : base(original, cloner) {
212      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
213      populationSizeAnalyzer = cloner.Clone(original.populationSizeAnalyzer);
214      offspringSuccessAnalyzer = cloner.Clone(original.offspringSuccessAnalyzer);
215      selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
216      Initialize();
217    }
218    public RAPGA()
219      : base() {
220      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
221      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
222      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
223      Parameters.Add(new ValueParameter<IntValue>("MinimumPopulationSize", "The minimum size of the population of solutions.", new IntValue(2)));
224      Parameters.Add(new ValueParameter<IntValue>("MaximumPopulationSize", "The maximum size of the population of solutions.", new IntValue(300)));
225      Parameters.Add(new ValueParameter<DoubleValue>("ComparisonFactor", "The comparison factor.", new DoubleValue(0.0)));
226      Parameters.Add(new ValueParameter<IntValue>("Effort", "The maximum number of offspring created in each generation.", new IntValue(1000)));
227      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)));
228      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
229      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
230      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
231      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
232      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
233      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 });
234      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
235      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
236      Parameters.Add(new ConstrainedValueParameter<ISingleObjectiveSolutionSimilarityCalculator>("SimilarityCalculator", "The operator used to calculate the similarity between two solutions."));
237
238      RandomCreator randomCreator = new RandomCreator();
239      SolutionsCreator solutionsCreator = new SolutionsCreator();
240      SubScopesCounter subScopesCounter = new SubScopesCounter();
241      ResultsCollector resultsCollector = new ResultsCollector();
242      RAPGAMainLoop mainLoop = new RAPGAMainLoop();
243      OperatorGraph.InitialOperator = randomCreator;
244
245      randomCreator.RandomParameter.ActualName = "Random";
246      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
247      randomCreator.SeedParameter.Value = null;
248      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
249      randomCreator.SetSeedRandomlyParameter.Value = null;
250      randomCreator.Successor = solutionsCreator;
251
252      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
253      solutionsCreator.Successor = subScopesCounter;
254
255      subScopesCounter.Name = "Initialize EvaluatedSolutions";
256      subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
257      subScopesCounter.Successor = resultsCollector;
258
259      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", null, "EvaluatedSolutions"));
260      resultsCollector.ResultsParameter.ActualName = "Results";
261      resultsCollector.Successor = mainLoop;
262
263      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
264      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
265      mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
266      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
267      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
268      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
269      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
270      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
271      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
272      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
273      mainLoop.PopulationSizeParameter.ActualName = PopulationSizeParameter.Name;
274      mainLoop.ResultsParameter.ActualName = "Results";
275
276      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
277        SelectorParameter.ValidValues.Add(selector);
278      ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
279      if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
280      ParameterizeSelectors();
281
282      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
283      populationSizeAnalyzer = new PopulationSizeAnalyzer();
284      offspringSuccessAnalyzer = new OffspringSuccessAnalyzer();
285      selectionPressureAnalyzer = new SelectionPressureAnalyzer();
286      ParameterizeAnalyzers();
287      UpdateAnalyzers();
288
289      Initialize();
290    }
291    public override IDeepCloneable Clone(Cloner cloner) {
292      return new RAPGA(this, cloner);
293    }
294
295    public override void Prepare() {
296      if (Problem != null && SimilarityCalculator != null) base.Prepare();
297    }
298
299    #region Events
300    protected override void OnProblemChanged() {
301      ParameterizeStochasticOperator(Problem.SolutionCreator);
302      ParameterizeStochasticOperator(Problem.Evaluator);
303      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
304      ParameterizeSolutionsCreator();
305      ParameterizeSelectors();
306      ParameterizeAnalyzers();
307      ParameterizeIterationBasedOperators();
308      UpdateCrossovers();
309      UpdateMutators();
310      UpdateAnalyzers();
311      UpdateSimilarityCalculators();
312      ParameterizeRAPGAMainLoop();
313      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
314      base.OnProblemChanged();
315    }
316
317    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
318      ParameterizeStochasticOperator(Problem.SolutionCreator);
319      ParameterizeSolutionsCreator();
320      base.Problem_SolutionCreatorChanged(sender, e);
321    }
322    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
323      ParameterizeStochasticOperator(Problem.Evaluator);
324      ParameterizeSolutionsCreator();
325      ParameterizeRAPGAMainLoop();
326      ParameterizeSelectors();
327      ParameterizeAnalyzers();
328      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
329      base.Problem_EvaluatorChanged(sender, e);
330    }
331    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
332      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
333      ParameterizeIterationBasedOperators();
334      UpdateCrossovers();
335      UpdateMutators();
336      UpdateAnalyzers();
337      UpdateSimilarityCalculators();
338      ParameterizeRAPGAMainLoop();
339      base.Problem_OperatorsChanged(sender, e);
340    }
341    private void SimilarityCalculatorParameter_ValueChanged(object sender, EventArgs e) {
342      ParameterizeRAPGAMainLoop();
343    }
344    private void BatchSizeParameter_ValueChanged(object sender, EventArgs e) {
345      BatchSize.ValueChanged += new EventHandler(BatchSize_ValueChanged);
346      ParameterizeSelectors();
347    }
348    private void BatchSize_ValueChanged(object sender, EventArgs e) {
349      ParameterizeSelectors();
350    }
351    private void ElitesParameter_ValueChanged(object sender, EventArgs e) {
352      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
353      ParameterizeSelectors();
354    }
355    private void Elites_ValueChanged(object sender, EventArgs e) {
356      ParameterizeSelectors();
357    }
358
359    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
360      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
361      ParameterizeSelectors();
362    }
363    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
364      ParameterizeSelectors();
365    }
366    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
367      ParameterizeRAPGAMainLoop();
368      ParameterizeSelectors();
369      ParameterizeAnalyzers();
370      ParameterizeSimilarityCalculators();
371    }
372    #endregion
373
374    #region Helpers
375    private void Initialize() {
376      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
377      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
378      ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged);
379      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
380      BatchSizeParameter.ValueChanged += new EventHandler(BatchSizeParameter_ValueChanged);
381      BatchSize.ValueChanged += new EventHandler(BatchSize_ValueChanged);
382      SimilarityCalculatorParameter.ValueChanged += new EventHandler(SimilarityCalculatorParameter_ValueChanged);
383      if (Problem != null) {
384        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
385      }
386    }
387
388    private void ParameterizeSolutionsCreator() {
389      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
390      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
391    }
392    private void ParameterizeRAPGAMainLoop() {
393      RAPGAMainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
394      RAPGAMainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
395      RAPGAMainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
396    }
397    private void ParameterizeStochasticOperator(IOperator op) {
398      IStochasticOperator stochasticOp = op as IStochasticOperator;
399      if (stochasticOp != null) {
400        stochasticOp.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
401        stochasticOp.RandomParameter.Hidden = true;
402      }
403    }
404    private void ParameterizeSelectors() {
405      foreach (ISelector selector in SelectorParameter.ValidValues) {
406        selector.CopySelected = new BoolValue(true);
407        selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * BatchSize.Value);
408        selector.NumberOfSelectedSubScopesParameter.Hidden = true;
409        ParameterizeStochasticOperator(selector);
410      }
411      if (Problem != null) {
412        foreach (ISingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType<ISingleObjectiveSelector>()) {
413          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
414          selector.MaximizationParameter.Hidden = true;
415          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
416          selector.QualityParameter.Hidden = true;
417        }
418      }
419    }
420    private void ParameterizeAnalyzers() {
421      qualityAnalyzer.ResultsParameter.ActualName = "Results";
422      qualityAnalyzer.ResultsParameter.Hidden = true;
423      populationSizeAnalyzer.ResultsParameter.ActualName = "Results";
424      populationSizeAnalyzer.ResultsParameter.Hidden = true;
425      offspringSuccessAnalyzer.ResultsParameter.ActualName = "Results";
426      offspringSuccessAnalyzer.ResultsParameter.Hidden = true;
427      selectionPressureAnalyzer.ResultsParameter.ActualName = "Results";
428      selectionPressureAnalyzer.ResultsParameter.Hidden = true;
429      if (Problem != null) {
430        qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
431        qualityAnalyzer.MaximizationParameter.Hidden = true;
432        qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
433        qualityAnalyzer.QualityParameter.Depth = 1;
434        qualityAnalyzer.QualityParameter.Hidden = true;
435        qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
436        qualityAnalyzer.BestKnownQualityParameter.Hidden = true;
437      }
438    }
439    private void ParameterizeIterationBasedOperators() {
440      if (Problem != null) {
441        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
442          op.IterationsParameter.ActualName = "Generations";
443          op.IterationsParameter.Hidden = true;
444          op.MaximumIterationsParameter.ActualName = "MaximumGenerations";
445          op.MaximumIterationsParameter.Hidden = true;
446        }
447      }
448    }
449    private void ParameterizeSimilarityCalculators() {
450      foreach (ISingleObjectiveSolutionSimilarityCalculator calc in SimilarityCalculatorParameter.ValidValues) {
451        calc.QualityVariableName = Problem.Evaluator.QualityParameter.ActualName;
452      }
453    }
454    private void UpdateCrossovers() {
455      ICrossover oldCrossover = CrossoverParameter.Value;
456      CrossoverParameter.ValidValues.Clear();
457      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
458
459      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
460        CrossoverParameter.ValidValues.Add(crossover);
461
462      if (oldCrossover != null) {
463        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
464        if (crossover != null) CrossoverParameter.Value = crossover;
465        else oldCrossover = null;
466      }
467      if (oldCrossover == null && defaultCrossover != null)
468        CrossoverParameter.Value = defaultCrossover;
469    }
470    private void UpdateMutators() {
471      IManipulator oldMutator = MutatorParameter.Value;
472      MutatorParameter.ValidValues.Clear();
473      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
474        MutatorParameter.ValidValues.Add(mutator);
475      if (oldMutator != null) {
476        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
477        if (mutator != null) MutatorParameter.Value = mutator;
478      }
479    }
480    private void UpdateAnalyzers() {
481      Analyzer.Operators.Clear();
482      if (Problem != null) {
483        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
484          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
485            param.Depth = 1;
486          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
487        }
488      }
489      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
490      Analyzer.Operators.Add(populationSizeAnalyzer, populationSizeAnalyzer.EnabledByDefault);
491      Analyzer.Operators.Add(offspringSuccessAnalyzer, offspringSuccessAnalyzer.EnabledByDefault);
492      Analyzer.Operators.Add(selectionPressureAnalyzer, selectionPressureAnalyzer.EnabledByDefault);
493    }
494    private void UpdateSimilarityCalculators() {
495      ISingleObjectiveSolutionSimilarityCalculator oldSimilarityCalculator = SimilarityCalculatorParameter.Value;
496      SimilarityCalculatorParameter.ValidValues.Clear();
497      ISingleObjectiveSolutionSimilarityCalculator defaultSimilarityCalculator = Problem.Operators.OfType<ISingleObjectiveSolutionSimilarityCalculator>().FirstOrDefault();
498
499      SimilarityCalculatorParameter.ValidValues.Add(new QualitySimilarityCalculator { QualityVariableName = Problem.Evaluator.QualityParameter.ActualName });
500      SimilarityCalculatorParameter.ValidValues.Add(new NoSimilarityCalculator());
501
502      foreach (ISingleObjectiveSolutionSimilarityCalculator similarityCalculator in Problem.Operators.OfType<ISingleObjectiveSolutionSimilarityCalculator>())
503        SimilarityCalculatorParameter.ValidValues.Add(similarityCalculator);
504
505      if (oldSimilarityCalculator != null) {
506        ISingleObjectiveSolutionSimilarityCalculator similarityCalculator = SimilarityCalculatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldSimilarityCalculator.GetType());
507        if (similarityCalculator != null) SimilarityCalculatorParameter.Value = similarityCalculator;
508        else oldSimilarityCalculator = null;
509      }
510      if (oldSimilarityCalculator == null && defaultSimilarityCalculator != null)
511        SimilarityCalculatorParameter.Value = defaultSimilarityCalculator;
512    }
513    private RAPGAMainLoop FindMainLoop(IOperator start) {
514      IOperator mainLoop = start;
515      while (mainLoop != null && !(mainLoop is RAPGAMainLoop))
516        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
517      if (mainLoop == null) return null;
518      else return (RAPGAMainLoop)mainLoop;
519    }
520    #endregion
521  }
522}
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