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source: branches/ScopedAlgorithms/HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm/3.3/OffspringSelectionGeneticAlgorithmMainOperator.cs @ 14529

Last change on this file since 14529 was 12012, checked in by ascheibe, 10 years ago

#2212 merged r12008, r12009, r12010 back into trunk
File size: 20.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using 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    public IValueLookupParameter<BoolValue> FillPopulationWithParentsParameter {
91      get { return (IValueLookupParameter<BoolValue>)Parameters["FillPopulationWithParents"]; }
92    }
93    #endregion
94
95    [StorableConstructor]
96    private OffspringSelectionGeneticAlgorithmMainOperator(bool deserializing) : base(deserializing) { }
97    private OffspringSelectionGeneticAlgorithmMainOperator(OffspringSelectionGeneticAlgorithmMainOperator original, Cloner cloner)
98      : base(original, cloner) {
99    }
100    public override IDeepCloneable Clone(Cloner cloner) {
101      return new OffspringSelectionGeneticAlgorithmMainOperator(this, cloner);
102    }
103    public OffspringSelectionGeneticAlgorithmMainOperator()
104      : base() {
105      Initialize();
106    }
107
108    [StorableHook(HookType.AfterDeserialization)]
109    private void AfterDeserialization() {
110      // BackwardsCompatibility3.3
111      #region Backwards compatible code, remove with 3.4
112      if (!Parameters.ContainsKey("ReevaluateElites")) {
113        Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
114      }
115      if (!Parameters.ContainsKey("FillPopulationWithParents"))
116        Parameters.Add(new ValueLookupParameter<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."));
117      #endregion
118    }
119
120    private void Initialize() {
121      #region Create parameters
122      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
123      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
124      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
125      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
126      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
127      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
128      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
129      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."));
130      Parameters.Add(new LookupParameter<IntValue>("EvaluatedSolutions", "The number of evaluated solutions."));
131      Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
132      Parameters.Add(new ValueLookupParameter<BoolValue>("ReevaluateElites", "Flag to determine if elite individuals should be reevaluated (i.e., if stochastic fitness functions are used.)"));
133      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]."));
134      Parameters.Add(new LookupParameter<DoubleValue>("CurrentSuccessRatio", "The current success ratio."));
135      Parameters.Add(new ValueLookupParameter<DoubleValue>("SuccessRatio", "The ratio of successful to total children that should be achieved."));
136      Parameters.Add(new LookupParameter<DoubleValue>("SelectionPressure", "The actual selection pressure."));
137      Parameters.Add(new ValueLookupParameter<DoubleValue>("MaximumSelectionPressure", "The maximum selection pressure that terminates the algorithm."));
138      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."));
139      Parameters.Add(new ValueLookupParameter<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."));
140      #endregion
141
142      #region Create operators
143      Placeholder selector = new Placeholder();
144      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
145      ChildrenCreator childrenCreator = new ChildrenCreator();
146      ConditionalBranch osBeforeMutationBranch = new ConditionalBranch();
147      UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
148      Placeholder crossover1 = new Placeholder();
149      UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
150      Placeholder evaluator1 = new Placeholder();
151      SubScopesCounter subScopesCounter1 = new SubScopesCounter();
152      WeightedParentsQualityComparator qualityComparer1 = new WeightedParentsQualityComparator();
153      SubScopesRemover subScopesRemover1 = new SubScopesRemover();
154      UniformSubScopesProcessor uniformSubScopesProcessor3 = new UniformSubScopesProcessor();
155      StochasticBranch mutationBranch1 = new StochasticBranch();
156      Placeholder mutator1 = new Placeholder();
157      VariableCreator variableCreator1 = new VariableCreator();
158      VariableCreator variableCreator2 = new VariableCreator();
159      ConditionalSelector conditionalSelector = new ConditionalSelector();
160      SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
161      UniformSubScopesProcessor uniformSubScopesProcessor4 = new UniformSubScopesProcessor();
162      Placeholder evaluator2 = new Placeholder();
163      SubScopesCounter subScopesCounter2 = new SubScopesCounter();
164      MergingReducer mergingReducer1 = new MergingReducer();
165      UniformSubScopesProcessor uniformSubScopesProcessor5 = new UniformSubScopesProcessor();
166      Placeholder crossover2 = new Placeholder();
167      StochasticBranch mutationBranch2 = new StochasticBranch();
168      Placeholder mutator2 = new Placeholder();
169      UniformSubScopesProcessor uniformSubScopesProcessor6 = new UniformSubScopesProcessor();
170      Placeholder evaluator3 = new Placeholder();
171      SubScopesCounter subScopesCounter3 = new SubScopesCounter();
172      WeightedParentsQualityComparator qualityComparer2 = new WeightedParentsQualityComparator();
173      SubScopesRemover subScopesRemover2 = new SubScopesRemover();
174      OffspringSelector offspringSelector = new OffspringSelector();
175      SubScopesProcessor subScopesProcessor3 = new SubScopesProcessor();
176      BestSelector bestSelector = new BestSelector();
177      WorstSelector worstSelector = new WorstSelector();
178      RightReducer rightReducer = new RightReducer();
179      LeftReducer leftReducer = new LeftReducer();
180      MergingReducer mergingReducer2 = new MergingReducer();
181      ConditionalBranch reevaluateElitesBranch = new ConditionalBranch();
182      UniformSubScopesProcessor uniformSubScopesProcessor7 = new UniformSubScopesProcessor();
183      Placeholder evaluator4 = new Placeholder();
184      SubScopesCounter subScopesCounter4 = new SubScopesCounter();
185
186      selector.Name = "Selector (placeholder)";
187      selector.OperatorParameter.ActualName = SelectorParameter.Name;
188
189      childrenCreator.ParentsPerChild = new IntValue(2);
190
191      osBeforeMutationBranch.Name = "Apply OS before mutation?";
192      osBeforeMutationBranch.ConditionParameter.ActualName = OffspringSelectionBeforeMutationParameter.Name;
193
194      crossover1.Name = "Crossover (placeholder)";
195      crossover1.OperatorParameter.ActualName = CrossoverParameter.Name;
196
197      uniformSubScopesProcessor2.Parallel.Value = true;
198
199      evaluator1.Name = "Evaluator (placeholder)";
200      evaluator1.OperatorParameter.ActualName = EvaluatorParameter.Name;
201
202      subScopesCounter1.Name = "Increment EvaluatedSolutions";
203      subScopesCounter1.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
204
205      qualityComparer1.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
206      qualityComparer1.LeftSideParameter.ActualName = QualityParameter.Name;
207      qualityComparer1.MaximizationParameter.ActualName = MaximizationParameter.Name;
208      qualityComparer1.RightSideParameter.ActualName = QualityParameter.Name;
209      qualityComparer1.ResultParameter.ActualName = "SuccessfulOffspring";
210
211      subScopesRemover1.RemoveAllSubScopes = true;
212
213      mutationBranch1.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
214      mutationBranch1.RandomParameter.ActualName = RandomParameter.Name;
215
216      mutator1.Name = "Mutator (placeholder)";
217      mutator1.OperatorParameter.ActualName = MutatorParameter.Name;
218
219      variableCreator1.Name = "MutatedOffspring = true";
220      variableCreator1.CollectedValues.Add(new ValueParameter<BoolValue>("MutatedOffspring", null, new BoolValue(true), false));
221
222      variableCreator2.Name = "MutatedOffspring = false";
223      variableCreator2.CollectedValues.Add(new ValueParameter<BoolValue>("MutatedOffspring", null, new BoolValue(false), false));
224
225      conditionalSelector.ConditionParameter.ActualName = "MutatedOffspring";
226      conditionalSelector.ConditionParameter.Depth = 1;
227      conditionalSelector.CopySelected.Value = false;
228
229      uniformSubScopesProcessor4.Parallel.Value = true;
230
231      evaluator2.Name = "Evaluator (placeholder)";
232      evaluator2.OperatorParameter.ActualName = EvaluatorParameter.Name;
233
234      subScopesCounter2.Name = "Increment EvaluatedSolutions";
235      subScopesCounter2.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
236
237      crossover2.Name = "Crossover (placeholder)";
238      crossover2.OperatorParameter.ActualName = CrossoverParameter.Name;
239
240      mutationBranch2.ProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
241      mutationBranch2.RandomParameter.ActualName = RandomParameter.Name;
242
243      mutator2.Name = "Mutator (placeholder)";
244      mutator2.OperatorParameter.ActualName = MutatorParameter.Name;
245
246      uniformSubScopesProcessor6.Parallel.Value = true;
247
248      evaluator3.Name = "Evaluator (placeholder)";
249      evaluator3.OperatorParameter.ActualName = EvaluatorParameter.Name;
250
251      subScopesCounter3.Name = "Increment EvaluatedSolutions";
252      subScopesCounter3.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
253
254      qualityComparer2.ComparisonFactorParameter.ActualName = ComparisonFactorParameter.Name;
255      qualityComparer2.LeftSideParameter.ActualName = QualityParameter.Name;
256      qualityComparer2.MaximizationParameter.ActualName = MaximizationParameter.Name;
257      qualityComparer2.RightSideParameter.ActualName = QualityParameter.Name;
258      qualityComparer2.ResultParameter.ActualName = "SuccessfulOffspring";
259
260      subScopesRemover2.RemoveAllSubScopes = true;
261
262      offspringSelector.CurrentSuccessRatioParameter.ActualName = CurrentSuccessRatioParameter.Name;
263      offspringSelector.MaximumSelectionPressureParameter.ActualName = MaximumSelectionPressureParameter.Name;
264      offspringSelector.SelectionPressureParameter.ActualName = SelectionPressureParameter.Name;
265      offspringSelector.SuccessRatioParameter.ActualName = SuccessRatioParameter.Name;
266      offspringSelector.OffspringPopulationParameter.ActualName = "OffspringPopulation";
267      offspringSelector.OffspringPopulationWinnersParameter.ActualName = "OffspringPopulationWinners";
268      offspringSelector.SuccessfulOffspringParameter.ActualName = "SuccessfulOffspring";
269      offspringSelector.FillPopulationWithParentsParameter.ActualName = FillPopulationWithParentsParameter.Name;
270
271      bestSelector.CopySelected = new BoolValue(false);
272      bestSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
273      bestSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
274      bestSelector.QualityParameter.ActualName = QualityParameter.Name;
275
276      worstSelector.CopySelected = new BoolValue(false);
277      worstSelector.MaximizationParameter.ActualName = MaximizationParameter.Name;
278      worstSelector.NumberOfSelectedSubScopesParameter.ActualName = ElitesParameter.Name;
279      worstSelector.QualityParameter.ActualName = QualityParameter.Name;
280
281      reevaluateElitesBranch.ConditionParameter.ActualName = "ReevaluateElites";
282      reevaluateElitesBranch.Name = "Reevaluate elites ?";
283
284      uniformSubScopesProcessor7.Parallel.Value = true;
285
286      evaluator4.Name = "Evaluator (placeholder)";
287      evaluator4.OperatorParameter.ActualName = EvaluatorParameter.Name;
288
289      subScopesCounter4.Name = "Increment EvaluatedSolutions";
290      subScopesCounter4.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
291      #endregion
292
293      #region Create operator graph
294      OperatorGraph.InitialOperator = selector;
295      selector.Successor = subScopesProcessor1;
296      subScopesProcessor1.Operators.Add(new EmptyOperator());
297      subScopesProcessor1.Operators.Add(childrenCreator);
298      subScopesProcessor1.Successor = offspringSelector;
299      childrenCreator.Successor = osBeforeMutationBranch;
300      osBeforeMutationBranch.TrueBranch = uniformSubScopesProcessor1;
301      osBeforeMutationBranch.FalseBranch = uniformSubScopesProcessor5;
302      osBeforeMutationBranch.Successor = null;
303      uniformSubScopesProcessor1.Operator = crossover1;
304      uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
305      crossover1.Successor = null;
306      uniformSubScopesProcessor2.Operator = evaluator1;
307      uniformSubScopesProcessor2.Successor = subScopesCounter1;
308      evaluator1.Successor = qualityComparer1;
309      qualityComparer1.Successor = subScopesRemover1;
310      subScopesRemover1.Successor = null;
311      subScopesCounter1.Successor = uniformSubScopesProcessor3;
312      uniformSubScopesProcessor3.Operator = mutationBranch1;
313      uniformSubScopesProcessor3.Successor = conditionalSelector;
314      mutationBranch1.FirstBranch = mutator1;
315      mutationBranch1.SecondBranch = variableCreator2;
316      mutationBranch1.Successor = null;
317      mutator1.Successor = variableCreator1;
318      variableCreator1.Successor = null;
319      variableCreator2.Successor = null;
320      conditionalSelector.Successor = subScopesProcessor2;
321      subScopesProcessor2.Operators.Add(new EmptyOperator());
322      subScopesProcessor2.Operators.Add(uniformSubScopesProcessor4);
323      subScopesProcessor2.Successor = mergingReducer1;
324      uniformSubScopesProcessor4.Operator = evaluator2;
325      uniformSubScopesProcessor4.Successor = subScopesCounter2;
326      evaluator2.Successor = null;
327      subScopesCounter2.Successor = null;
328      mergingReducer1.Successor = null;
329      uniformSubScopesProcessor5.Operator = crossover2;
330      uniformSubScopesProcessor5.Successor = uniformSubScopesProcessor6;
331      crossover2.Successor = mutationBranch2;
332      mutationBranch2.FirstBranch = mutator2;
333      mutationBranch2.SecondBranch = null;
334      mutationBranch2.Successor = null;
335      mutator2.Successor = null;
336      uniformSubScopesProcessor6.Operator = evaluator3;
337      uniformSubScopesProcessor6.Successor = subScopesCounter3;
338      evaluator3.Successor = qualityComparer2;
339      qualityComparer2.Successor = subScopesRemover2;
340      subScopesRemover2.Successor = null;
341      subScopesCounter3.Successor = null;
342      offspringSelector.OffspringCreator = selector;
343      offspringSelector.Successor = subScopesProcessor3;
344      subScopesProcessor3.Operators.Add(bestSelector);
345      subScopesProcessor3.Operators.Add(worstSelector);
346      subScopesProcessor3.Successor = mergingReducer2;
347      bestSelector.Successor = rightReducer;
348      rightReducer.Successor = reevaluateElitesBranch;
349      reevaluateElitesBranch.TrueBranch = uniformSubScopesProcessor7;
350      uniformSubScopesProcessor7.Operator = evaluator4;
351      uniformSubScopesProcessor7.Successor = subScopesCounter4;
352      subScopesCounter4.Successor = null;
353      reevaluateElitesBranch.FalseBranch = null;
354      reevaluateElitesBranch.Successor = null;
355      worstSelector.Successor = leftReducer;
356      leftReducer.Successor = null;
357      mergingReducer2.Successor = null;
358      #endregion
359    }
360
361    public override IOperation Apply() {
362      if (CrossoverParameter.ActualValue == null)
363        return null;
364      return base.Apply();
365    }
366  }
367}
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