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source: stable/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithm.cs @ 11761

Last change on this file since 11761 was 11170, checked in by ascheibe, 10 years ago

#2115 updated copyright year in stable branch
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2014 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.OffspringSelectionGeneticAlgorithm {
37  /// <summary>
38  /// An offspring selection genetic algorithm.
39  /// </summary>
40  [Item("Offspring Selection Genetic Algorithm", "An offspring selection genetic algorithm (Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press).")]
41  [Creatable("Algorithms")]
42  [StorableClass]
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    private ValueParameter<IntValue> SeedParameter {
58      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
59    }
60    private ValueParameter<BoolValue> SetSeedRandomlyParameter {
61      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
62    }
63    private ValueParameter<IntValue> PopulationSizeParameter {
64      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }
65    }
66    public IConstrainedValueParameter<ISelector> SelectorParameter {
67      get { return (IConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
68    }
69    public IConstrainedValueParameter<ICrossover> CrossoverParameter {
70      get { return (IConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
71    }
72    private ValueParameter<PercentValue> MutationProbabilityParameter {
73      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
74    }
75    public IConstrainedValueParameter<IManipulator> MutatorParameter {
76      get { return (IConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
77    }
78    private ValueParameter<IntValue> ElitesParameter {
79      get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
80    }
81    private IFixedValueParameter<BoolValue> ReevaluateElitesParameter {
82      get { return (IFixedValueParameter<BoolValue>)Parameters["ReevaluateElites"]; }
83    }
84    private ValueParameter<IntValue> MaximumGenerationsParameter {
85      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
86    }
87    private ValueLookupParameter<DoubleValue> SuccessRatioParameter {
88      get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
89    }
90    private ValueLookupParameter<DoubleValue> ComparisonFactorLowerBoundParameter {
91      get { return (ValueLookupParameter<DoubleValue>)Parameters["ComparisonFactorLowerBound"]; }
92    }
93    private ValueLookupParameter<DoubleValue> ComparisonFactorUpperBoundParameter {
94      get { return (ValueLookupParameter<DoubleValue>)Parameters["ComparisonFactorUpperBound"]; }
95    }
96    public IConstrainedValueParameter<IDiscreteDoubleValueModifier> ComparisonFactorModifierParameter {
97      get { return (IConstrainedValueParameter<IDiscreteDoubleValueModifier>)Parameters["ComparisonFactorModifier"]; }
98    }
99    private ValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
100      get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
101    }
102    private ValueLookupParameter<BoolValue> OffspringSelectionBeforeMutationParameter {
103      get { return (ValueLookupParameter<BoolValue>)Parameters["OffspringSelectionBeforeMutation"]; }
104    }
105    private ValueLookupParameter<IntValue> SelectedParentsParameter {
106      get { return (ValueLookupParameter<IntValue>)Parameters["SelectedParents"]; }
107    }
108    private ValueParameter<MultiAnalyzer> AnalyzerParameter {
109      get { return (ValueParameter<MultiAnalyzer>)Parameters["Analyzer"]; }
110    }
111    private ValueParameter<IntValue> MaximumEvaluatedSolutionsParameter {
112      get { return (ValueParameter<IntValue>)Parameters["MaximumEvaluatedSolutions"]; }
113    }
114    private 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(bool deserializing) : base(deserializing) { }
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      #endregion
231
232      Initialize();
233    }
234    private OffspringSelectionGeneticAlgorithm(OffspringSelectionGeneticAlgorithm original, Cloner cloner)
235      : base(original, cloner) {
236      qualityAnalyzer = cloner.Clone(original.qualityAnalyzer);
237      selectionPressureAnalyzer = cloner.Clone(original.selectionPressureAnalyzer);
238      successfulOffspringAnalyzer = cloner.Clone(original.successfulOffspringAnalyzer);
239      Initialize();
240    }
241    public override IDeepCloneable Clone(Cloner cloner) {
242      return new OffspringSelectionGeneticAlgorithm(this, cloner);
243    }
244    public OffspringSelectionGeneticAlgorithm()
245      : base() {
246      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
247      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
248      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
249      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
250      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
251      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
252      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
253      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
254      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 });
255      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
256      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved.", new DoubleValue(1)));
257      Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorLowerBound", "The lower bound of the comparison factor (start).", new DoubleValue(0)));
258      Parameters.Add(new ValueLookupParameter<DoubleValue>("ComparisonFactorUpperBound", "The upper bound of the comparison factor (end).", new DoubleValue(1)));
259      Parameters.Add(new OptionalConstrainedValueParameter<IDiscreteDoubleValueModifier>("ComparisonFactorModifier", "The operator used to modify the comparison factor.", new ItemSet<IDiscreteDoubleValueModifier>(new IDiscreteDoubleValueModifier[] { new LinearDiscreteDoubleValueModifier() }), new LinearDiscreteDoubleValueModifier()));
260      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm.", new DoubleValue(100)));
261      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)));
262      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)));
263      Parameters.Add(new ValueParameter<MultiAnalyzer>("Analyzer", "The operator used to analyze each generation.", new MultiAnalyzer()));
264      Parameters.Add(new ValueParameter<IntValue>("MaximumEvaluatedSolutions", "The maximum number of evaluated solutions (approximately).", new IntValue(int.MaxValue)));
265      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 });
266
267      RandomCreator randomCreator = new RandomCreator();
268      SolutionsCreator solutionsCreator = new SolutionsCreator();
269      SubScopesCounter subScopesCounter = new SubScopesCounter();
270      ResultsCollector resultsCollector = new ResultsCollector();
271      OffspringSelectionGeneticAlgorithmMainLoop mainLoop = new OffspringSelectionGeneticAlgorithmMainLoop();
272      OperatorGraph.InitialOperator = randomCreator;
273
274      randomCreator.RandomParameter.ActualName = "Random";
275      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
276      randomCreator.SeedParameter.Value = null;
277      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
278      randomCreator.SetSeedRandomlyParameter.Value = null;
279      randomCreator.Successor = solutionsCreator;
280
281      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
282      solutionsCreator.Successor = subScopesCounter;
283
284      subScopesCounter.Name = "Initialize EvaluatedSolutions";
285      subScopesCounter.ValueParameter.ActualName = "EvaluatedSolutions";
286      subScopesCounter.Successor = resultsCollector;
287
288      resultsCollector.CollectedValues.Add(new LookupParameter<IntValue>("Evaluated Solutions", "", "EvaluatedSolutions"));
289      resultsCollector.ResultsParameter.ActualName = "Results";
290      resultsCollector.Successor = mainLoop;
291
292      mainLoop.AnalyzerParameter.ActualName = AnalyzerParameter.Name;
293      mainLoop.ComparisonFactorModifierParameter.ActualName = ComparisonFactorModifierParameter.Name;
294      mainLoop.ComparisonFactorParameter.ActualName = "ComparisonFactor";
295      mainLoop.ComparisonFactorStartParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
296      mainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
297      mainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
298      mainLoop.ReevaluateElitesParameter.ActualName = ReevaluateElitesParameter.Name;
299      mainLoop.EvaluatedSolutionsParameter.ActualName = "EvaluatedSolutions";
300      mainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
301      mainLoop.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
302      mainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
303      mainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
304      mainLoop.OffspringSelectionBeforeMutationParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
305      mainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
306      mainLoop.ResultsParameter.ActualName = "Results";
307      mainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
308      mainLoop.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
309      mainLoop.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
310
311      foreach (ISelector selector in ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name))
312        SelectorParameter.ValidValues.Add(selector);
313      ISelector proportionalSelector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("ProportionalSelector"));
314      if (proportionalSelector != null) SelectorParameter.Value = proportionalSelector;
315      ParameterizeSelectors();
316
317      foreach (IDiscreteDoubleValueModifier modifier in ApplicationManager.Manager.GetInstances<IDiscreteDoubleValueModifier>().OrderBy(x => x.Name))
318        ComparisonFactorModifierParameter.ValidValues.Add(modifier);
319      IDiscreteDoubleValueModifier linearModifier = ComparisonFactorModifierParameter.ValidValues.FirstOrDefault(x => x.GetType().Name.Equals("LinearDiscreteDoubleValueModifier"));
320      if (linearModifier != null) ComparisonFactorModifierParameter.Value = linearModifier;
321      ParameterizeComparisonFactorModifiers();
322
323      qualityAnalyzer = new BestAverageWorstQualityAnalyzer();
324      selectionPressureAnalyzer = new ValueAnalyzer();
325      successfulOffspringAnalyzer = new SuccessfulOffspringAnalyzer();
326      ParameterizeAnalyzers();
327      UpdateAnalyzers();
328
329      Initialize();
330    }
331
332
333
334    public override void Prepare() {
335      if (Problem != null) base.Prepare();
336    }
337
338    #region Events
339    protected override void OnProblemChanged() {
340      ParameterizeStochasticOperator(Problem.SolutionCreator);
341      ParameterizeStochasticOperator(Problem.Evaluator);
342      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
343      ParameterizeSolutionsCreator();
344      ParameterizMainLoop();
345      ParameterizeSelectors();
346      ParameterizeAnalyzers();
347      ParameterizeIterationBasedOperators();
348      UpdateCrossovers();
349      UpdateMutators();
350      UpdateAnalyzers();
351      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
352      base.OnProblemChanged();
353    }
354
355    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
356      ParameterizeStochasticOperator(Problem.SolutionCreator);
357      ParameterizeSolutionsCreator();
358      base.Problem_SolutionCreatorChanged(sender, e);
359    }
360    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
361      ParameterizeStochasticOperator(Problem.Evaluator);
362      ParameterizeSolutionsCreator();
363      ParameterizMainLoop();
364      ParameterizeSelectors();
365      ParameterizeAnalyzers();
366      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
367      base.Problem_EvaluatorChanged(sender, e);
368    }
369    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
370      foreach (IOperator op in Problem.Operators.OfType<IOperator>()) ParameterizeStochasticOperator(op);
371      ParameterizeIterationBasedOperators();
372      UpdateCrossovers();
373      UpdateMutators();
374      UpdateAnalyzers();
375      base.Problem_OperatorsChanged(sender, e);
376    }
377    private void ElitesParameter_ValueChanged(object sender, EventArgs e) {
378      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
379      ParameterizeSelectors();
380    }
381    private void Elites_ValueChanged(object sender, EventArgs e) {
382      ParameterizeSelectors();
383    }
384    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
385      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
386      ParameterizeSelectors();
387    }
388    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
389      ParameterizeSelectors();
390    }
391    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
392      ParameterizMainLoop();
393      ParameterizeSelectors();
394      ParameterizeAnalyzers();
395    }
396    #endregion
397
398    #region Helpers
399    private void Initialize() {
400      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
401      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
402      ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged);
403      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
404      if (Problem != null) {
405        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
406      }
407    }
408    private void ParameterizeSolutionsCreator() {
409      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
410      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
411    }
412    private void ParameterizMainLoop() {
413      MainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
414      MainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
415      MainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
416    }
417    private void ParameterizeStochasticOperator(IOperator op) {
418      if (op is IStochasticOperator)
419        ((IStochasticOperator)op).RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
420    }
421    private void ParameterizeSelectors() {
422      foreach (ISelector selector in SelectorParameter.ValidValues) {
423        selector.CopySelected = new BoolValue(true);
424        selector.NumberOfSelectedSubScopesParameter.Value = null;
425        selector.NumberOfSelectedSubScopesParameter.ActualName = SelectedParentsParameter.Name;
426        ParameterizeStochasticOperator(selector);
427      }
428      if (Problem != null) {
429        foreach (ISingleObjectiveSelector selector in SelectorParameter.ValidValues.OfType<ISingleObjectiveSelector>()) {
430          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
431          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
432        }
433      }
434    }
435    private void ParameterizeAnalyzers() {
436      qualityAnalyzer.ResultsParameter.ActualName = "Results";
437      selectionPressureAnalyzer.Name = "SelectionPressure Analyzer";
438      selectionPressureAnalyzer.ResultsParameter.ActualName = "Results";
439      selectionPressureAnalyzer.ValueParameter.ActualName = "SelectionPressure";
440      selectionPressureAnalyzer.ValueParameter.Depth = 0;
441      selectionPressureAnalyzer.ValuesParameter.ActualName = "Selection Pressure History";
442      successfulOffspringAnalyzer.ResultsParameter.ActualName = "Results";
443      successfulOffspringAnalyzer.GenerationsParameter.ActualName = "Generations";
444      successfulOffspringAnalyzer.SuccessfulOffspringFlagParameter.Value.Value = "SuccessfulOffspring";
445      successfulOffspringAnalyzer.DepthParameter.Value = new IntValue(1);
446      if (Problem != null) {
447        qualityAnalyzer.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
448        qualityAnalyzer.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
449        qualityAnalyzer.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
450      }
451    }
452    private void ParameterizeComparisonFactorModifiers() {
453      foreach (IDiscreteDoubleValueModifier modifier in ComparisonFactorModifierParameter.ValidValues) {
454        modifier.IndexParameter.ActualName = "Generations";
455        modifier.EndIndexParameter.ActualName = MaximumGenerationsParameter.Name;
456        modifier.EndValueParameter.ActualName = ComparisonFactorUpperBoundParameter.Name;
457        modifier.StartIndexParameter.Value = new IntValue(0);
458        modifier.StartValueParameter.ActualName = ComparisonFactorLowerBoundParameter.Name;
459        modifier.ValueParameter.ActualName = "ComparisonFactor";
460      }
461    }
462    private void ParameterizeIterationBasedOperators() {
463      if (Problem != null) {
464        foreach (IIterationBasedOperator op in Problem.Operators.OfType<IIterationBasedOperator>()) {
465          op.IterationsParameter.ActualName = "Generations";
466          op.MaximumIterationsParameter.ActualName = MaximumGenerationsParameter.Name;
467        }
468      }
469    }
470    private void UpdateCrossovers() {
471      ICrossover oldCrossover = CrossoverParameter.Value;
472      CrossoverParameter.ValidValues.Clear();
473      ICrossover defaultCrossover = Problem.Operators.OfType<ICrossover>().FirstOrDefault();
474
475      foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
476        CrossoverParameter.ValidValues.Add(crossover);
477
478      if (oldCrossover != null) {
479        ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
480        if (crossover != null) CrossoverParameter.Value = crossover;
481        else oldCrossover = null;
482      }
483      if (oldCrossover == null && defaultCrossover != null)
484        CrossoverParameter.Value = defaultCrossover;
485    }
486    private void UpdateMutators() {
487      IManipulator oldMutator = MutatorParameter.Value;
488      MutatorParameter.ValidValues.Clear();
489      foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
490        MutatorParameter.ValidValues.Add(mutator);
491      if (oldMutator != null) {
492        IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
493        if (mutator != null) MutatorParameter.Value = mutator;
494      }
495    }
496    private void UpdateAnalyzers() {
497      Analyzer.Operators.Clear();
498      if (Problem != null) {
499        foreach (IAnalyzer analyzer in Problem.Operators.OfType<IAnalyzer>()) {
500          foreach (IScopeTreeLookupParameter param in analyzer.Parameters.OfType<IScopeTreeLookupParameter>())
501            param.Depth = 1;
502          Analyzer.Operators.Add(analyzer, analyzer.EnabledByDefault);
503        }
504      }
505      Analyzer.Operators.Add(qualityAnalyzer, qualityAnalyzer.EnabledByDefault);
506      Analyzer.Operators.Add(selectionPressureAnalyzer, selectionPressureAnalyzer.EnabledByDefault);
507      Analyzer.Operators.Add(successfulOffspringAnalyzer, successfulOffspringAnalyzer.EnabledByDefault);
508    }
509    private OffspringSelectionGeneticAlgorithmMainLoop FindMainLoop(IOperator start) {
510      IOperator mainLoop = start;
511      while (mainLoop != null && !(mainLoop is OffspringSelectionGeneticAlgorithmMainLoop))
512        mainLoop = ((SingleSuccessorOperator)mainLoop).Successor;
513      if (mainLoop == null) return null;
514      else return (OffspringSelectionGeneticAlgorithmMainLoop)mainLoop;
515    }
516    #endregion
517  }
518}
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