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source: trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithmMainOperator.cs @ 10543

Last change on this file since 10543 was 9592, checked in by abeham, 11 years ago

#2038: Added tagging comment
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Operators;
26using HeuristicLab.Optimization.Operators;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Selection;
30
31namespace HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm {
32  /// <summary>
33  /// An operator which represents the main loop of an offspring selection genetic algorithm.
34  /// </summary>
35  [Item("OffspringSelectionGeneticAlgorithmMainOperator", "An operator that represents the core of an offspring selection genetic algorithm.")]
36  [StorableClass]
37  public sealed class OffspringSelectionGeneticAlgorithmMainOperator : AlgorithmOperator {
38    #region Parameter properties
39    public ValueLookupParameter<IRandom> RandomParameter {
40      get { return (ValueLookupParameter<IRandom>)Parameters["Random"]; }
41    }
42    public ValueLookupParameter<BoolValue> MaximizationParameter {
43      get { return (ValueLookupParameter<BoolValue>)Parameters["Maximization"]; }
44    }
45    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
46      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters["Quality"]; }
47    }
48    public ValueLookupParameter<IOperator> SelectorParameter {
49      get { return (ValueLookupParameter<IOperator>)Parameters["Selector"]; }
50    }
51    public ValueLookupParameter<IOperator> CrossoverParameter {
52      get { return (ValueLookupParameter<IOperator>)Parameters["Crossover"]; }
53    }
54    public ValueLookupParameter<PercentValue> MutationProbabilityParameter {
55      get { return (ValueLookupParameter<PercentValue>)Parameters["MutationProbability"]; }
56    }
57    public ValueLookupParameter<IOperator> MutatorParameter {
58      get { return (ValueLookupParameter<IOperator>)Parameters["Mutator"]; }
59    }
60    public ValueLookupParameter<IOperator> EvaluatorParameter {
61      get { return (ValueLookupParameter<IOperator>)Parameters["Evaluator"]; }
62    }
63    public LookupParameter<IntValue> EvaluatedSolutionsParameter {
64      get { return (LookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
65    }
66    public ValueLookupParameter<IntValue> ElitesParameter {
67      get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
68    }
69    public IValueLookupParameter<BoolValue> ReevaluateElitesParameter {
70      get { return (IValueLookupParameter<BoolValue>)Parameters["ReevaluateElites"]; }
71    }
72    public LookupParameter<DoubleValue> ComparisonFactorParameter {
73      get { return (LookupParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
74    }
75    public LookupParameter<DoubleValue> CurrentSuccessRatioParameter {
76      get { return (LookupParameter<DoubleValue>)Parameters["CurrentSuccessRatio"]; }
77    }
78    public ValueLookupParameter<DoubleValue> SuccessRatioParameter {
79      get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
80    }
81    public LookupParameter<DoubleValue> SelectionPressureParameter {
82      get { return (LookupParameter<DoubleValue>)Parameters["SelectionPressure"]; }
83    }
84    public ValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
85      get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
86    }
87    public ValueLookupParameter<BoolValue> OffspringSelectionBeforeMutationParameter {
88      get { return (ValueLookupParameter<BoolValue>)Parameters["OffspringSelectionBeforeMutation"]; }
89    }
90    #endregion
91
92    [StorableConstructor]
93    private OffspringSelectionGeneticAlgorithmMainOperator(bool deserializing) : base(deserializing) { }
94    private OffspringSelectionGeneticAlgorithmMainOperator(OffspringSelectionGeneticAlgorithmMainOperator original, Cloner cloner)
95      : base(original, cloner) {
96    }
97    public override IDeepCloneable Clone(Cloner cloner) {
98      return new OffspringSelectionGeneticAlgorithmMainOperator(this, cloner);
99    }
100    public OffspringSelectionGeneticAlgorithmMainOperator()
101      : base() {
102      Initialize();
103    }
104
105    [StorableHook(HookType.AfterDeserialization)]
106    private void AfterDeserialization() {
107      // BackwardsCompatibility3.3
108      #region Backwards compatible code, remove with 3.4
109      if (!Parameters.ContainsKey("ReevaluateElites")) {
110        Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
111      }
112      #endregion
113    }
114
115    private void Initialize() {
116      #region Create parameters
117      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
118      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
119      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
120      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
121      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
122      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
123      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
124      Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions. This operator is executed in parallel, if an engine is used which supports parallelization."));
125      Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
126      Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
127      Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
128      Parameters.Add(new LookupParameter<DoubleValue>("ComparisonFactor", "The comparison factor is used to determine whether the offspring should be compared to the better parent, the worse parent or a quality value linearly interpolated between them. It is in the range [0;1]."));
129      Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
130      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved."));
131      Parameters.Add(new LookupParameter<DoubleValue>("SelectionPressure", "The actual selection pressure."));
132      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm."));
133      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."));
134      #endregion
135
136      #region Create operators
137      Placeholder selector = new Placeholder();
138      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
139      ChildrenCreator childrenCreator = new ChildrenCreator();
140      ConditionalBranch osBeforeMutationBranch = new ConditionalBranch();
141      UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
142      Placeholder crossover1 = new Placeholder();
143      UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
144      Placeholder evaluator1 = new Placeholder();
145      SubScopesCounter subScopesCounter1 = new SubScopesCounter();
146      WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator();
147      SubScopesRemover subScopesRemover1 = new SubScopesRemover();
148      UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor();
149      StochasticBranch mutationBranch1 = new StochasticBranch();
150      Placeholder mutator1 = new Placeholder();
151      VariableCreator variableCreator1 = new VariableCreator();
152      VariableCreator variableCreator2 = new VariableCreator();
153      ConditionalSelector conditionalSelector = new ConditionalSelector();
154      SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
155      UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor();
156      Placeholder evaluator2 = new Placeholder();
157      SubScopesCounter subScopesCounter2 = new SubScopesCounter();
158      MergingReducer mergingReducer1 = new MergingReducer();
159      UniformSubScopesProcessor uniformSubScopesProcessor5 = new UniformSubScopesProcessor();
160      Placeholder crossover2 = new Placeholder();
161      StochasticBranch mutationBranch2 = new StochasticBranch();
162      Placeholder mutator2 = new Placeholder();
163      UniformSubScopesProcessor uniformSubScopesProcessor6 = new UniformSubScopesProcessor();
164      Placeholder evaluator3 = new Placeholder();
165      SubScopesCounter subScopesCounter3 = new SubScopesCounter();
166      WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator();
167      SubScopesRemover subScopesRemover2 = new SubScopesRemover();
168      OffspringSelector offspringSelector = new OffspringSelector();
169      SubScopesProcessor subScopesProcessor3 = new SubScopesProcessor();
170      BestSelector bestSelector = new BestSelector();
171      WorstSelector worstSelector = new WorstSelector();
172      RightReducer rightReducer = new RightReducer();
173      LeftReducer leftReducer = new LeftReducer();
174      MergingReducer mergingReducer2 = new MergingReducer();
175      ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
176      UniformSubScopesProcessor uniformSubScopesProcessor7 = new UniformSubScopesProcessor();
177      Placeholder evaluator4 = new Placeholder();
178      SubScopesCounter subScopesCounter4 = new SubScopesCounter();
179
180      selector.Name = "Selector (placeholder)";
181      selector.OperatorParameter.ActualName = SelectorParameter.Name;
182
183      childrenCreator.ParentsPerChild = new IntValue(2);
184
185      osBeforeMutationBranch.Name = "Apply OS before mutation?";
186      osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
187
188      crossover1.Name = "Crossover (placeholder)";
189      crossover1.OperatorParameter.ActualName = CrossoverParameter.Name;
190
191      uniformSubScopesProcessor2.Parallel.Value = true;
192
193      evaluator1.Name = "Evaluator (placeholder)";
194      evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name;
195
196      subScopesCounter1.Name = "Increment EvaluatedSolutions";
197      subScopesCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
198
199      qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
200      qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name;
201      qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name;
202      qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name;
203      qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring";
204
205      subScopesRemover1.RemoveAllSubScopes = true;
206
207      mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
208      mutationBranch1.RandomParameter.ActualName = RandomParameter.Name;
209
210      mutator1.Name = "Mutator (placeholder)";
211      mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
212
213      variableCreator1.Name = "MutatedOffspring = true";
214      variableCreator1.CollectedValues.Add(new ValueParameter<BoolValue>("MutatedOffspring", null, new BoolValue(true), false));
215
216      variableCreator2.Name = "MutatedOffspring = false";
217      variableCreator2.CollectedValues.Add(new ValueParameter<BoolValue>("MutatedOffspring", null, new BoolValue(false), false));
218
219      conditionalSelector.ConditionParameter.ActualName = "MutatedOffspring";
220      conditionalSelector.ConditionParameter.Depth = 1;
221      conditionalSelector.CopySelected.Value = false;
222
223      uniformSubScopesProcessor4.Parallel.Value = true;
224
225      evaluator2.Name = "Evaluator (placeholder)";
226      evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
227
228      subScopesCounter2.Name = "Increment EvaluatedSolutions";
229      subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
230
231      crossover2.Name = "Crossover (placeholder)";
232      crossover2.OperatorParameter.ActualName = CrossoverParameter.Name;
233
234      mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
235      mutationBranch2.RandomParameter.ActualName = RandomParameter.Name;
236
237      mutator2.Name = "Mutator (placeholder)";
238      mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
239
240      uniformSubScopesProcessor6.Parallel.Value = true;
241
242      evaluator3.Name = "Evaluator (placeholder)";
243      evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name;
244
245      subScopesCounter3.Name = "Increment EvaluatedSolutions";
246      subScopesCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
247
248      qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
249      qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name;
250      qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name;
251      qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name;
252      qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring";
253
254      subScopesRemover2.RemoveAllSubScopes = true;
255
256      offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
257      offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
258      offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
259      offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
260      offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation";
261      offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners";
262      offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring";
263
264      bestSelector.CopySelected = new BoolValue(false);
265      bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
266      bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
267      bestSelector.QualityParameter.ActualName = QualityParameter.Name;
268
269      worstSelector.CopySelected = new BoolValue(false);
270      worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
271      worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
272      worstSelector.QualityParameter.ActualName = QualityParameter.Name;
273
274      reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
275      reevaluateElitesBranch.Name = "Reevaluate elites ?";
276
277      uniformSubScopesProcessor7.Parallel.Value = true;
278
279      evaluator4.Name = "Evaluator (placeholder)";
280      evaluator4.OperatorParameter.ActualName = EvaluatorParameter.Name;
281
282      subScopesCounter4.Name = "Increment EvaluatedSolutions";
283      subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
284      #endregion
285
286      #region Create operator graph
287      OperatorGraph.InitialOperator = selector;
288      selector.Successor = subScopesProcessor1;
289      subScopesProcessor1.Operators.Add(new EmptyOperator());
290      subScopesProcessor1.Operators.Add(childrenCreator);
291      subScopesProcessor1.Successor = offspringSelector;
292      childrenCreator.Successor = osBeforeMutationBranch;
293      osBeforeMutationBranch.TrueBranch = uniformSubScopesProcessor1;
294      osBeforeMutationBranch.FalseBranch = uniformSubScopesProcessor5;
295      osBeforeMutationBranch.Successor = null;
296      uniformSubScopesProcessor1.Operator = crossover1;
297      uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
298      crossover1.Successor = null;
299      uniformSubScopesProcessor2.Operator = evaluator1;
300      uniformSubScopesProcessor2.Successor = subScopesCounter1;
301      evaluator1.Successor = qualityComparer1;
302      qualityComparer1.Successor = subScopesRemover1;
303      subScopesRemover1.Successor = null;
304      subScopesCounter1.Successor = uniformSubScopesProcessor3;
305      uniformSubScopesProcessor3.Operator = mutationBranch1;
306      uniformSubScopesProcessor3.Successor = conditionalSelector;
307      mutationBranch1.FirstBranch = mutator1;
308      mutationBranch1.SecondBranch = variableCreator2;
309      mutationBranch1.Successor = null;
310      mutator1.Successor = variableCreator1;
311      variableCreator1.Successor = null;
312      variableCreator2.Successor = null;
313      conditionalSelector.Successor = subScopesProcessor2;
314      subScopesProcessor2.Operators.Add(new EmptyOperator());
315      subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4);
316      subScopesProcessor2.Successor = mergingReducer1;
317      uniformSubScopesProcessor4.Operator = evaluator2;
318      uniformSubScopesProcessor4.Successor = subScopesCounter2;
319      evaluator2.Successor = null;
320      subScopesCounter2.Successor = null;
321      mergingReducer1.Successor = null;
322      uniformSubScopesProcessor5.Operator = crossover2;
323      uniformSubScopesProcessor5.Successor = uniformSubScopesProcessor6;
324      crossover2.Successor = mutationBranch2;
325      mutationBranch2.FirstBranch = mutator2;
326      mutationBranch2.SecondBranch = null;
327      mutationBranch2.Successor = null;
328      mutator2.Successor = null;
329      uniformSubScopesProcessor6.Operator = evaluator3;
330      uniformSubScopesProcessor6.Successor = subScopesCounter3;
331      evaluator3.Successor = qualityComparer2;
332      qualityComparer2.Successor = subScopesRemover2;
333      subScopesRemover2.Successor = null;
334      subScopesCounter3.Successor = null;
335      offspringSelector.OffspringCreator = selector;
336      offspringSelector.Successor = subScopesProcessor3;
337      subScopesProcessor3.Operators.Add(bestSelector);
338      subScopesProcessor3.Operators.Add(worstSelector);
339      subScopesProcessor3.Successor = mergingReducer2;
340      bestSelector.Successor = rightReducer;
341      rightReducer.Successor = reevaluateElitesBranch;
342      reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor7;
343      uniformSubScopesProcessor7.Operator = evaluator4;
344      uniformSubScopesProcessor7.Successor = subScopesCounter4;
345      subScopesCounter4.Successor = null;
346      reevaluateElitesBranch.FalseBranch = null;
347      reevaluateElitesBranch.Successor = null;
348      worstSelector.Successor = leftReducer;
349      leftReducer.Successor = null;
350      mergingReducer2.Successor = null;
351      #endregion
352    }
353
354    public override IOperation Apply() {
355      if (CrossoverParameter.ActualValue == null)
356        return null;
357      return base.Apply();
358    }
359  }
360}
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