source: trunk/sources/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithmMainOperator.cs @ 3713

Last change on this file since 3713 was 3713, checked in by abeham, 11 years ago

#839, #893, #976

  • changed elites replacement in all offspring selection algorithms
File size: 14.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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.Analysis;
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 LookupParameter<DoubleValue> ComparisonFactorParameter {
70      get { return (LookupParameter<DoubleValue>)Parameters["ComparisonFactor"]; }
71    }
72    public LookupParameter<DoubleValue> CurrentSuccessRatioParameter {
73      get { return (LookupParameter<DoubleValue>)Parameters["CurrentSuccessRatio"]; }
74    }
75    public ValueLookupParameter<DoubleValue> SuccessRatioParameter {
76      get { return (ValueLookupParameter<DoubleValue>)Parameters["SuccessRatio"]; }
77    }
78    public LookupParameter<DoubleValue> SelectionPressureParameter {
79      get { return (LookupParameter<DoubleValue>)Parameters["SelectionPressure"]; }
80    }
81    public ValueLookupParameter<DoubleValue> MaximumSelectionPressureParameter {
82      get { return (ValueLookupParameter<DoubleValue>)Parameters["MaximumSelectionPressure"]; }
83    }
84    public ValueLookupParameter<BoolValue> OffspringSelectionBeforeMutationParameter {
85      get { return (ValueLookupParameter<BoolValue>)Parameters["OffspringSelectionBeforeMutation"]; }
86    }
87    #endregion
88
89    [StorableConstructor]
90    private OffspringSelectionGeneticAlgorithmMainOperator(bool deserializing) : base() { }
91    public OffspringSelectionGeneticAlgorithmMainOperator()
92      : base() {
93      Initialize();
94    }
95
96    private void Initialize() {
97      #region Create parameters
98      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
99      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
100      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
101      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
102      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
103      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
104      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
105      Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions."));
106      Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
107      Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
108      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]."));
109      Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
110      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved."));
111      Parameters.Add(new LookupParameter<DoubleValue>("SelectionPressure", "The actual selection pressure."));
112      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm."));
113      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."));
114      #endregion
115
116      #region Create operators
117      Placeholder selector = new Placeholder();
118      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
119      ChildrenCreator childrenCreator = new ChildrenCreator();
120      UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
121      Placeholder crossover = new Placeholder();
122      ConditionalBranch osBeforeMutationBranch = new ConditionalBranch();
123      Placeholder evaluator1 = new Placeholder();
124      IntCounter evaluationCounter1 = new IntCounter();
125      WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator();
126      StochasticBranch mutationBranch1 = new StochasticBranch();
127      Placeholder mutator1 = new Placeholder();
128      Placeholder evaluator2 = new Placeholder();
129      IntCounter evaluationCounter2 = new IntCounter();
130      StochasticBranch mutationBranch2 = new StochasticBranch();
131      Placeholder mutator2 = new Placeholder();
132      Placeholder evaluator3 = new Placeholder();
133      IntCounter evaluationCounter3 = new IntCounter();
134      WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator();
135      SubScopesRemover subScopesRemover = new SubScopesRemover();
136      ConditionalSelector conditionalSelector = new ConditionalSelector();
137      OffspringSelector offspringSelector = new OffspringSelector();
138      SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
139      BestSelector bestSelector = new BestSelector();
140      WorstSelector worstSelector = new WorstSelector();
141      RightReducer rightReducer = new RightReducer();
142      LeftReducer leftReducer = new LeftReducer();
143      MergingReducer mergingReducer = new MergingReducer();
144     
145      selector.Name = "Selector (placeholder)";
146      selector.OperatorParameter.ActualName = SelectorParameter.Name;
147
148      childrenCreator.ParentsPerChild = new IntValue(2);
149
150      crossover.Name = "Crossover (placeholder)";
151      crossover.OperatorParameter.ActualName = CrossoverParameter.Name;
152
153      osBeforeMutationBranch.Name = "Apply OS before mutation?";
154      osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
155
156      evaluator1.Name = "Evaluator (placeholder)";
157      evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name;
158
159      evaluationCounter1.Name = "EvaluatedSolutions++";
160      evaluationCounter1.Increment = new IntValue(1);
161      evaluationCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
162
163      qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
164      qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name;
165      qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name;
166      qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name;
167      qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring";
168
169      mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
170      mutationBranch1.RandomParameter.ActualName = RandomParameter.Name;
171
172      mutator1.Name = "Mutator (placeholder)";
173      mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
174
175      evaluator2.Name = "Evaluator (placeholder)";
176      evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
177
178      evaluationCounter2.Name = "EvaluatedSolutions++";
179      evaluationCounter2.Increment = new IntValue(1);
180      evaluationCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
181
182      mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
183      mutationBranch2.RandomParameter.ActualName = RandomParameter.Name;
184
185      mutator2.Name = "Mutator (placeholder)";
186      mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
187
188      evaluator3.Name = "Evaluator (placeholder)";
189      evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name;
190
191      evaluationCounter3.Name = "EvaluatedSolutions++";
192      evaluationCounter3.Increment = new IntValue(1);
193      evaluationCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
194
195      qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
196      qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name;
197      qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name;
198      qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name;
199      qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring";
200
201      subScopesRemover.RemoveAllSubScopes = true;
202
203      conditionalSelector.CopySelected = new BoolValue(false);
204      conditionalSelector.ConditionParameter.ActualName = "SuccessfulOffspring";
205
206      offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
207      offspringSelector.LuckyLosersParameter.ActualName = "OSLuckyLosers";
208      offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
209      offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
210      offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
211      offspringSelector.WinnersParameter.ActualName = "OSWinners";
212
213      bestSelector.CopySelected = new BoolValue(false);
214      bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
215      bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
216      bestSelector.QualityParameter.ActualName = QualityParameter.Name;
217
218      worstSelector.CopySelected = new BoolValue(false);
219      worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
220      worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
221      worstSelector.QualityParameter.ActualName = QualityParameter.Name;
222      #endregion
223
224      #region Create operator graph
225      OperatorGraph.InitialOperator = selector;
226      selector.Successor = subScopesProcessor1;
227      subScopesProcessor1.Operators.Add(new EmptyOperator());
228      subScopesProcessor1.Operators.Add(childrenCreator);
229      subScopesProcessor1.Successor = offspringSelector;
230      childrenCreator.Successor = uniformSubScopesProcessor;
231      uniformSubScopesProcessor.Operator = crossover;
232      uniformSubScopesProcessor.Successor = conditionalSelector;
233      crossover.Successor = osBeforeMutationBranch;
234      osBeforeMutationBranch.TrueBranch = evaluator1;
235      osBeforeMutationBranch.FalseBranch = mutationBranch2;
236      osBeforeMutationBranch.Successor = subScopesRemover;
237      evaluator1.Successor = evaluationCounter1;
238      evaluationCounter1.Successor = qualityComparer1;
239      qualityComparer1.Successor = mutationBranch1;
240      mutationBranch1.FirstBranch = mutator1;
241      mutationBranch1.SecondBranch = null;
242      mutationBranch1.Successor = null;
243      mutator1.Successor = evaluator2;
244      evaluator2.Successor = evaluationCounter2;
245      evaluationCounter2.Successor = null;
246      mutationBranch2.FirstBranch = mutator2;
247      mutationBranch2.SecondBranch = null;
248      mutationBranch2.Successor = evaluator3;
249      mutator2.Successor = null;
250      evaluator3.Successor = evaluationCounter3;
251      evaluationCounter3.Successor = qualityComparer2;
252      subScopesRemover.Successor = null;
253      offspringSelector.OffspringCreator = selector;
254      offspringSelector.Successor = subScopesProcessor2;
255      subScopesProcessor2.Operators.Add(bestSelector);
256      subScopesProcessor2.Operators.Add(worstSelector);
257      subScopesProcessor2.Successor = mergingReducer;
258      bestSelector.Successor = rightReducer;
259      rightReducer.Successor = null;
260      worstSelector.Successor = leftReducer;
261      leftReducer.Successor = null;
262      mergingReducer.Successor = null;
263      #endregion
264    }
265  }
266}
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