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source: stable/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithm.cs @ 17318

Last change on this file since 17318 was 17181, checked in by swagner, 5 years ago

#2875: Merged r17180 from trunk to stable
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HEAL.Attic;
33using HeuristicLab.PluginInfrastructure;
34using HeuristicLab.Random;
35
36namespace HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm {
37  /// <summary>
38  /// An offspring selection genetic algorithm.
39  /// </summary>
40  [Item("Offspring Selection Genetic Algorithm (OSGA)", "An offspring selection genetic algorithm (Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press).")]
41  [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 120)]
42  [StorableType("7D0CF428-70B3-407F-A2AF-D544F115C998")]
43  public sealed class OffspringSelectionGeneticAlgorithm : 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    public IValueParameter<IntValue> SeedParameter {
58      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
59    }
60    public IValueParameter<BoolValue> SetSeedRandomlyParameter {
61      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
62    }
63    public IValueParameter<IntValue> PopulationSizeParameter {
64      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }
65    }
66    public IConstrainedValueParameter<ISelector> SelectorParameter {
67      get { return (IConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
68    }
69    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
70      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
71    }
72    public IValueParameter<PercentValue> MutationProbabilityParameter {
73      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
74    }
75    public IConstrainedValueParameter<IManipulator> MutatorParameter {
76      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
77    }
78    public IValueParameter<IntValue> ElitesParameter {
79      get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
80    }
81    public IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
82      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
83    }
84    public IValueParameter<IntValue> MaximumGenerationsParameter {
85      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
86    }
87    public IValueLookupParameter<DoubleValue> SuccessRatioParameter {
88      get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
89    }
90    public IValueLookupParameter<DoubleValue> ComparisonFactorLowerBoundParameter {
91      get { return (ValueLookupParameter<DoubleValue>)Parameters["ComparisonFactorLowerBound"]; }
92    }
93    public IValueLookupParameter<DoubleValue> ComparisonFactorUpperBoundParameter {
94      get { return (ValueLookupParameter<DoubleValue>)Parameters["ComparisonFactorUpperBound"]; }
95    }
96    public IConstrainedValueParameter<IDiscreteDoubleValueModifier> ComparisonFactorModifierParameter {
97      get { return (IConstrainedValueParameter<IDiscreteDoubleValueModifier>)Parameters["ComparisonFactorModifier"]; }
98    }
99    public IValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
100      get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
101    }
102    public IValueLookupParameter<BoolValue> OffspringSelectionBeforeMutationParameter {
103      get { return (ValueLookupParameter<BoolValue>)Parameters["OffspringSelectionBeforeMutation"]; }
104    }
105    public IValueLookupParameter<IntValue> SelectedParentsParameter {
106      get { return (ValueLookupParameter<IntValue>)Parameters["SelectedParents"]; }
107    }
108    public IValueParameter<MultiAnalyzer> AnalyzerParameter {
109      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
110    }
111    public IValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
112      get { return (ValueParameter<IntValue>)Parameters["MaximumEvaluatedSolutions"]; }
113    }
114    public IFixedValueParameter<BoolValue> FillPopulationWithParentsParameter {
115      get { return (IFixedValueParameter<BoolValue>)Parameters["FillPopulationWithParents"]; }
116    }
117    #endregion
118
119    #region Properties
120    public IntValue Seed {
121      get { return SeedParameter.Value; }
122      set { SeedParameter.Value = value; }
123    }
124    public BoolValue SetSeedRandomly {
125      get { return SetSeedRandomlyParameter.Value; }
126      set { SetSeedRandomlyParameter.Value = value; }
127    }
128    public IntValue PopulationSize {
129      get { return PopulationSizeParameter.Value; }
130      set { PopulationSizeParameter.Value = value; }
131    }
132    public ISelector Selector {
133      get { return SelectorParameter.Value; }
134      set { SelectorParameter.Value = value; }
135    }
136    public ICrossover Crossover {
137      get { return CrossoverParameter.Value; }
138      set { CrossoverParameter.Value = value; }
139    }
140    public PercentValue MutationProbability {
141      get { return MutationProbabilityParameter.Value; }
142      set { MutationProbabilityParameter.Value = value; }
143    }
144    public IManipulator Mutator {
145      get { return MutatorParameter.Value; }
146      set { MutatorParameter.Value = value; }
147    }
148    public IntValue Elites {
149      get { return ElitesParameter.Value; }
150      set { ElitesParameter.Value = value; }
151    }
152    public bool ReevaluteElites {
153      get { return ReevaluateElitesParameter.Value.Value; }
154      set { ReevaluateElitesParameter.Value.Value = value; }
155    }
156    public IntValue MaximumGenerations {
157      get { return MaximumGenerationsParameter.Value; }
158      set { MaximumGenerationsParameter.Value = value; }
159    }
160    public DoubleValue SuccessRatio {
161      get { return SuccessRatioParameter.Value; }
162      set { SuccessRatioParameter.Value = value; }
163    }
164    public DoubleValue ComparisonFactorLowerBound {
165      get { return ComparisonFactorLowerBoundParameter.Value; }
166      set { ComparisonFactorLowerBoundParameter.Value = value; }
167    }
168    public DoubleValue ComparisonFactorUpperBound {
169      get { return ComparisonFactorUpperBoundParameter.Value; }
170      set { ComparisonFactorUpperBoundParameter.Value = value; }
171    }
172    public IDiscreteDoubleValueModifier ComparisonFactorModifier {
173      get { return ComparisonFactorModifierParameter.Value; }
174      set { ComparisonFactorModifierParameter.Value = value; }
175    }
176    public DoubleValue MaximumSelectionPressure {
177      get { return MaximumSelectionPressureParameter.Value; }
178      set { MaximumSelectionPressureParameter.Value = value; }
179    }
180    public BoolValue OffspringSelectionBeforeMutation {
181      get { return OffspringSelectionBeforeMutationParameter.Value; }
182      set { OffspringSelectionBeforeMutationParameter.Value = value; }
183    }
184    public IntValue SelectedParents {
185      get { return SelectedParentsParameter.Value; }
186      set { SelectedParentsParameter.Value = value; }
187    }
188    public MultiAnalyzer Analyzer {
189      get { return AnalyzerParameter.Value; }
190      set { AnalyzerParameter.Value = value; }
191    }
192    public IntValue MaximumEvaluatedSolutions {
193      get { return MaximumEvaluatedSolutionsParameter.Value; }
194      set { MaximumEvaluatedSolutionsParameter.Value = value; }
195    }
196    public bool FillPopulationWithParents {
197      get { return FillPopulationWithParentsParameter.Value.Value; }
198      set { FillPopulationWithParentsParameter.Value.Value = value; }
199    }
200    private RandomCreator RandomCreator {
201      get { return (RandomCreator)OperatorGraph.InitialOperator; }
202    }
203    private SolutionsCreator SolutionsCreator {
204      get { return (SolutionsCreator)RandomCreator.Successor; }
205    }
206    private OffspringSelectionGeneticAlgorithmMainLoop MainLoop {
207      get { return FindMainLoop(SolutionsCreator.Successor); }
208    }
209    [Storable]
210    private BestAverageWorstQualityAnalyzer qualityAnalyzer;
211    [Storable]
212    private ValueAnalyzer selectionPressureAnalyzer;
213    [Storable]
214    private SuccessfulOffspringAnalyzer successfulOffspringAnalyzer;
215    #endregion
216
217    [StorableConstructor]
218    private OffspringSelectionGeneticAlgorithm(StorableConstructorFlag _) : base(_) { }
219    [StorableHook(HookType.AfterDeserialization)]
220    private void AfterDeserialization() {
221      // BackwardsCompatibility3.3
222      #region Backwards compatible code, remove with 3.4
223      if (successfulOffspringAnalyzer == null)
224        successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
225      if (!Parameters.ContainsKey("ReevaluateElites")) {
226        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 });
227      }
228      if (!Parameters.ContainsKey("FillPopulationWithParents"))
229        Parameters.Add(new FixedValueParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded.", new BoolValue(false)) { Hidden = true });
230
231      var optionalMutatorParameter = MutatorParameter as OptionalConstrainedValueParameter<IManipulator>;
232      if (optionalMutatorParameter != null) {
233        Parameters.Remove(optionalMutatorParameter);
234        Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
235        foreach (var m in optionalMutatorParameter.ValidValues)
236          MutatorParameter.ValidValues.Add(m);
237        if (optionalMutatorParameter.Value == null) MutationProbability.Value = 0; // to guarantee that the old configuration results in the same behavior
238        else Mutator = optionalMutatorParameter.Value;
239        optionalMutatorParameter.ValidValues.Clear(); // to avoid dangling references to the old parameter its valid values are cleared
240      }
241      #endregion
242
243      Initialize();
244    }
245    private OffspringSelectionGeneticAlgorithm(OffspringSelectionGeneticAlgorithm original, Cloner cloner)
246      : base(original, cloner) {
247      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
248      selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
249      successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
250      Initialize();
251    }
252    public override IDeepCloneable Clone(Cloner cloner) {
253      return new OffspringSelectionGeneticAlgorithm(this, cloner);
254    }
255    public OffspringSelectionGeneticAlgorithm()
256      : base() {
257      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
258      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
259      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
260      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
261      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
262      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
263      Parameters.Add(new ConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
264      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
265      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 });
266      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
267      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
268      Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0)));
269      Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(1)));
270      Parameters.Add(new OptionalConstrainedValueParameter<IDiscreteDoubleValueModifier>("ComparisonFactorModifier", "The operator used to modify the comparison factor.", new ItemSet<IDiscreteDoubleValueModifier>(new IDiscreteDoubleValueModifier[] { new LinearDiscreteDoubleValueModifier() }), new LinearDiscreteDoubleValueModifier()));
271      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
272      Parameters.Add(new ValueLookupParameter<BoolValue>("OffspringSelectionBeforeMutation", "True if the offspring selection step should be applied before mutation, false if it should be applied after mutation.", new BoolValue(false)));
273      Parameters.Add(new ValueLookupParameter<IntValue>("SelectedParents", "How much parents should be selected each time the offspring selection step is performed until the population is filled. This parameter should be about the same or twice the size of PopulationSize for smaller problems, and less for large problems.", new IntValue(200)));
274      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
275      Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions (approximately).", new IntValue(int.MaxValue)));
276      Parameters.Add(new FixedValueParameter<BoolValue>("FillPopulationWithParents", "True if the population should be filled with parent individual or false if worse children should be used when the maximum selection pressure is exceeded.", new BoolValue(false)) { Hidden = true });
277
278      RandomCreator randomCreator = new RandomCreator();
279      SolutionsCreator solutionsCreator = new SolutionsCreator();
280      SubScopesCounter subScopesCounter = new SubScopesCounter();
281      ResultsCollector resultsCollector = new ResultsCollector();
282      OffspringSelectionGeneticAlgorithmMainLoop mainLoop = new OffspringSelectionGeneticAlgorithmMainLoop();
283      OperatorGraph.InitialOperator = randomCreator;
284
285      randomCreator.RandomParameter.ActualName = "Random";
286      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
287      randomCreator.SeedParameter.Value = null;
288      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
289      randomCreator.SetSeedRandomlyParameter.Value = null;
290      randomCreator.Successor = solutionsCreator;
291
292      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
293      solutionsCreator.Successor = subScopesCounter;
294
295      subScopesCounter.Name = "Initialize EvaluatedSolutions";
296      subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
297      subScopesCounter.Successor = resultsCollector;
298
299      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", "", "EvaluatedSolutions"));
300      resultsCollector.ResultsParameter.ActualName = "Results";
301      resultsCollector.Successor = mainLoop;
302
303      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
304      mainLoop.ComparisonFactorModifierParameter.ActualName = ComparisonFactorModifierParameter.Name;
305      mainLoop.ComparisonFactorParameter.ActualName = "ComparisonFactor";
306      mainLoop.ComparisonFactorStartParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
307      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
308      mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
309      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
310      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
311      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
312      mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
313      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
314      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
315      mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
316      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
317      mainLoop.ResultsParameter.ActualName = "Results";
318      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
319      mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
320      mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
321
322      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
323        SelectorParameter.ValidValues.Add(selector);
324      ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
325      if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
326      ParameterizeSelectors();
327
328      foreach (IDiscreteDoubleValueModifier modifier in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
329        ComparisonFactorModifierParameter.ValidValues.Add(modifier);
330      IDiscreteDoubleValueModifier linearModifier = ComparisonFactorModifierParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("LinearDiscreteDoubleValueModifier"));
331      if (linearModifier != null) ComparisonFactorModifierParameter.Value = linearModifier;
332      ParameterizeComparisonFactorModifiers();
333
334      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
335      selectionPressureAnalyzer = new ValueAnalyzer();
336      successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
337      ParameterizeAnalyzers();
338      UpdateAnalyzers();
339
340      Initialize();
341    }
342
343
344
345    public override void Prepare() {
346      if (Problem != null) base.Prepare();
347    }
348
349    #region Events
350    protected override void OnProblemChanged() {
351      ParameterizeStochasticOperator(Problem.SolutionCreator);
352      ParameterizeStochasticOperator(Problem.Evaluator);
353      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
354      ParameterizeSolutionsCreator();
355      ParameterizMainLoop();
356      ParameterizeSelectors();
357      ParameterizeAnalyzers();
358      ParameterizeIterationBasedOperators();
359      UpdateCrossovers();
360      UpdateMutators();
361      UpdateAnalyzers();
362      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
363      base.OnProblemChanged();
364    }
365
366    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
367      ParameterizeStochasticOperator(Problem.SolutionCreator);
368      ParameterizeSolutionsCreator();
369      base.Problem_SolutionCreatorChanged(sender, e);
370    }
371    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
372      ParameterizeStochasticOperator(Problem.Evaluator);
373      ParameterizeSolutionsCreator();
374      ParameterizMainLoop();
375      ParameterizeSelectors();
376      ParameterizeAnalyzers();
377      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
378      base.Problem_EvaluatorChanged(sender, e);
379    }
380    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
381      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
382      ParameterizeIterationBasedOperators();
383      UpdateCrossovers();
384      UpdateMutators();
385      UpdateAnalyzers();
386      base.Problem_OperatorsChanged(sender, e);
387    }
388    private void ElitesParameter_ValueChanged(object sender, EventArgs e) {
389      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
390      ParameterizeSelectors();
391    }
392    private void Elites_ValueChanged(object sender, EventArgs e) {
393      ParameterizeSelectors();
394    }
395    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
396      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
397      ParameterizeSelectors();
398    }
399    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
400      ParameterizeSelectors();
401    }
402    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
403      ParameterizMainLoop();
404      ParameterizeSelectors();
405      ParameterizeAnalyzers();
406    }
407    #endregion
408
409    #region Helpers
410    private void Initialize() {
411      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
412      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
413      ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged);
414      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
415      if (Problem != null) {
416        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
417      }
418    }
419    private void ParameterizeSolutionsCreator() {
420      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
421      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
422    }
423    private void ParameterizMainLoop() {
424      MainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
425      MainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
426      MainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
427    }
428    private void ParameterizeStochasticOperator(IOperator op) {
429      if (op is IStochasticOperator)
430        ((IStochasticOperator)op).RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
431    }
432    private void ParameterizeSelectors() {
433      foreach (ISelector selector in SelectorParameter.ValidValues) {
434        selector.CopySelected = new BoolValue(true);
435        selector.NumberOfSelectedSubScopesParameter.Value = null;
436        selector.NumberOfSelectedSubScopesParameter.ActualName = SelectedParentsParameter.Name;
437        ParameterizeStochasticOperator(selector);
438      }
439      if (Problem != null) {
440        foreach (ISingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType<ISingleObjectiveSelector>()) {
441          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
442          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
443        }
444      }
445    }
446    private void ParameterizeAnalyzers() {
447      qualityAnalyzer.ResultsParameter.ActualName = "Results";
448      selectionPressureAnalyzer.Name = "SelectionPressure Analyzer";
449      selectionPressureAnalyzer.ResultsParameter.ActualName = "Results";
450      selectionPressureAnalyzer.ValueParameter.ActualName = "SelectionPressure";
451      selectionPressureAnalyzer.ValueParameter.Depth = 0;
452      selectionPressureAnalyzer.ValuesParameter.ActualName = "Selection Pressure History";
453      successfulOffspringAnalyzer.ResultsParameter.ActualName = "Results";
454      successfulOffspringAnalyzer.GenerationsParameter.ActualName = "Generations";
455      successfulOffspringAnalyzer.SuccessfulOffspringFlagParameter.Value.Value = "SuccessfulOffspring";
456      successfulOffspringAnalyzer.DepthParameter.Value = new IntValue(1);
457      if (Problem != null) {
458        qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
459        qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
460        qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
461      }
462    }
463    private void ParameterizeComparisonFactorModifiers() {
464      foreach (IDiscreteDoubleValueModifier modifier in ComparisonFactorModifierParameter.ValidValues) {
465        modifier.IndexParameter.ActualName = "Generations";
466        modifier.EndIndexParameter.ActualName = MaximumGenerationsParameter.Name;
467        modifier.EndValueParameter.ActualName = ComparisonFactorUpperBoundParameter.Name;
468        modifier.StartIndexParameter.Value = new IntValue(0);
469        modifier.StartValueParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
470        modifier.ValueParameter.ActualName = "ComparisonFactor";
471      }
472    }
473    private void ParameterizeIterationBasedOperators() {
474      if (Problem != null) {
475        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
476          op.IterationsParameter.ActualName = "Generations";
477          op.MaximumIterationsParameter.ActualName = MaximumGenerationsParameter.Name;
478        }
479      }
480    }
481    private void UpdateCrossovers() {
482      ICrossover oldCrossover = CrossoverParameter.Value;
483      CrossoverParameter.ValidValues.Clear();
484      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
485
486      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
487        CrossoverParameter.ValidValues.Add(crossover);
488
489      if (oldCrossover != null) {
490        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
491        if (crossover != null) CrossoverParameter.Value = crossover;
492        else oldCrossover = null;
493      }
494      if (oldCrossover == null && defaultCrossover != null)
495        CrossoverParameter.Value = defaultCrossover;
496    }
497    private void UpdateMutators() {
498      IManipulator oldMutator = MutatorParameter.Value;
499      MutatorParameter.ValidValues.Clear();
500      IManipulator defaultMutator = Problem.Operators.OfType<IManipulator>().FirstOrDefault();
501
502      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
503        MutatorParameter.ValidValues.Add(mutator);
504
505      if (oldMutator != null) {
506        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
507        if (mutator != null) MutatorParameter.Value = mutator;
508        else oldMutator = null;
509      }
510
511      if (oldMutator == null && defaultMutator != null)
512        MutatorParameter.Value = defaultMutator;
513    }
514    private void UpdateAnalyzers() {
515      Analyzer.Operators.Clear();
516      if (Problem != null) {
517        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
518          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
519            param.Depth = 1;
520          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
521        }
522      }
523      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
524      Analyzer.Operators.Add(selectionPressureAnalyzer, selectionPressureAnalyzer.EnabledByDefault);
525      Analyzer.Operators.Add(successfulOffspringAnalyzer, successfulOffspringAnalyzer.EnabledByDefault);
526    }
527    private OffspringSelectionGeneticAlgorithmMainLoop FindMainLoop(IOperator start) {
528      IOperator mainLoop = start;
529      while (mainLoop != null && !(mainLoop is OffspringSelectionGeneticAlgorithmMainLoop))
530        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
531      if (mainLoop == null) return null;
532      else return (OffspringSelectionGeneticAlgorithmMainLoop)mainLoop;
533    }
534    #endregion
535  }
536}
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