Free cookie consent management tool by TermsFeed Policy Generator

source: trunk/sources/HeuristicLab.Algorithms.GeneticAlgorithm/3.3/GeneticAlgorithmMainLoop.cs @ 6514

Last change on this file since 6514 was 5445, checked in by swagner, 14 years ago

Updated year of copyrights (#1406)
File size: 11.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.GeneticAlgorithm {
32  /// <summary>
33  /// An operator which represents the main loop of a genetic algorithm.
34  /// </summary>
35  [Item("GeneticAlgorithmMainLoop", "An operator which represents the main loop of a genetic algorithm.")]
36  [StorableClass]
37  public sealed class GeneticAlgorithmMainLoop : 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 ValueLookupParameter<IntValue> ElitesParameter {
64      get { return (ValueLookupParameter<IntValue>)Parameters["Elites"]; }
65    }
66    public ValueLookupParameter<IntValue> MaximumGenerationsParameter {
67      get { return (ValueLookupParameter<IntValue>)Parameters["MaximumGenerations"]; }
68    }
69    public ValueLookupParameter<VariableCollection> ResultsParameter {
70      get { return (ValueLookupParameter<VariableCollection>)Parameters["Results"]; }
71    }
72    public ValueLookupParameter<IOperator> AnalyzerParameter {
73      get { return (ValueLookupParameter<IOperator>)Parameters["Analyzer"]; }
74    }
75    public ValueLookupParameter<IntValue> EvaluatedSolutionsParameter {
76      get { return (ValueLookupParameter<IntValue>)Parameters["EvaluatedSolutions"]; }
77    }
78    public ValueLookupParameter<IntValue> PopulationSizeParameter {
79      get { return (ValueLookupParameter<IntValue>)Parameters["PopulationSize"]; }
80    }
81    private ScopeParameter CurrentScopeParameter {
82      get { return (ScopeParameter)Parameters["CurrentScope"]; }
83    }
84
85    public IScope CurrentScope {
86      get { return CurrentScopeParameter.ActualValue; }
87    }
88    #endregion
89
90    [StorableConstructor]
91    private GeneticAlgorithmMainLoop(bool deserializing) : base(deserializing) { }
92    private GeneticAlgorithmMainLoop(GeneticAlgorithmMainLoop original, Cloner cloner)
93      : base(original, cloner) {
94    }
95    public override IDeepCloneable Clone(Cloner cloner) {
96      return new GeneticAlgorithmMainLoop(this, cloner);
97    }
98    public GeneticAlgorithmMainLoop()
99      : base() {
100      Initialize();
101    }
102
103    private void Initialize() {
104      #region Create parameters
105      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
106      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
107      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
108      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
109      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
110      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
111      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
112      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."));
113      Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
114      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
115      Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
116      Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
117      Parameters.Add(new ValueLookupParameter<IntValue>("EvaluatedSolutions", "The number of times solutions have been evaluated."));
118      Parameters.Add(new ValueLookupParameter<IntValue>("PopulationSize", "The size of the population."));
119      Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied."));
120      #endregion
121
122      #region Create operators
123      VariableCreator variableCreator = new VariableCreator();
124      ResultsCollector resultsCollector1 = new ResultsCollector();
125      Placeholder analyzer1 = new Placeholder();
126      Placeholder selector = new Placeholder();
127      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
128      ChildrenCreator childrenCreator = new ChildrenCreator();
129      UniformSubScopesProcessor uniformSubScopesProcessor1 = new UniformSubScopesProcessor();
130      Placeholder crossover = new Placeholder();
131      StochasticBranch stochasticBranch = new StochasticBranch();
132      Placeholder mutator = new Placeholder();
133      SubScopesRemover subScopesRemover = new SubScopesRemover();
134      UniformSubScopesProcessor uniformSubScopesProcessor2 = new UniformSubScopesProcessor();
135      Placeholder evaluator = new Placeholder();
136      SubScopesCounter subScopesCounter = new SubScopesCounter();
137      SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
138      BestSelector bestSelector = new BestSelector();
139      RightReducer rightReducer = new RightReducer();
140      MergingReducer mergingReducer = new MergingReducer();
141      IntCounter intCounter = new IntCounter();
142      Comparator comparator = new Comparator();
143      Placeholder analyzer2 = new Placeholder();
144      ConditionalBranch conditionalBranch = new ConditionalBranch();
145
146      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class GeneticAlgorithm expects this to be called Generations
147
148      resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
149      resultsCollector1.ResultsParameter.ActualName = "Results";
150
151      analyzer1.Name = "Analyzer";
152      analyzer1.OperatorParameter.ActualName = "Analyzer";
153
154      selector.Name = "Selector";
155      selector.OperatorParameter.ActualName = "Selector";
156
157      childrenCreator.ParentsPerChild = new IntValue(2);
158
159      crossover.Name = "Crossover";
160      crossover.OperatorParameter.ActualName = "Crossover";
161
162      stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability";
163      stochasticBranch.RandomParameter.ActualName = "Random";
164
165      mutator.Name = "Mutator";
166      mutator.OperatorParameter.ActualName = "Mutator";
167
168      subScopesRemover.RemoveAllSubScopes = true;
169
170      uniformSubScopesProcessor2.Parallel.Value = true;
171
172      evaluator.Name = "Evaluator";
173      evaluator.OperatorParameter.ActualName = "Evaluator";
174
175      subScopesCounter.Name = "Increment EvaluatedSolutions";
176      subScopesCounter.ValueParameter.ActualName = EvaluatedSolutionsParameter.Name;
177
178      bestSelector.CopySelected = new BoolValue(false);
179      bestSelector.MaximizationParameter.ActualName = "Maximization";
180      bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites";
181      bestSelector.QualityParameter.ActualName = "Quality";
182
183      intCounter.Increment = new IntValue(1);
184      intCounter.ValueParameter.ActualName = "Generations";
185
186      comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
187      comparator.LeftSideParameter.ActualName = "Generations";
188      comparator.ResultParameter.ActualName = "Terminate";
189      comparator.RightSideParameter.ActualName = "MaximumGenerations";
190
191      analyzer2.Name = "Analyzer";
192      analyzer2.OperatorParameter.ActualName = "Analyzer";
193
194      conditionalBranch.ConditionParameter.ActualName = "Terminate";
195      #endregion
196
197      #region Create operator graph
198      OperatorGraph.InitialOperator = variableCreator;
199      variableCreator.Successor = resultsCollector1;
200      resultsCollector1.Successor = analyzer1;
201      analyzer1.Successor = selector;
202      selector.Successor = subScopesProcessor1;
203      subScopesProcessor1.Operators.Add(new EmptyOperator());
204      subScopesProcessor1.Operators.Add(childrenCreator);
205      subScopesProcessor1.Successor = subScopesProcessor2;
206      childrenCreator.Successor = uniformSubScopesProcessor1;
207      uniformSubScopesProcessor1.Operator = crossover;
208      uniformSubScopesProcessor1.Successor = uniformSubScopesProcessor2;
209      crossover.Successor = stochasticBranch;
210      stochasticBranch.FirstBranch = mutator;
211      stochasticBranch.SecondBranch = null;
212      stochasticBranch.Successor = subScopesRemover;
213      mutator.Successor = null;
214      subScopesRemover.Successor = null;
215      uniformSubScopesProcessor2.Operator = evaluator;
216      uniformSubScopesProcessor2.Successor = subScopesCounter;
217      evaluator.Successor = null;
218      subScopesCounter.Successor = null;
219      subScopesProcessor2.Operators.Add(bestSelector);
220      subScopesProcessor2.Operators.Add(new EmptyOperator());
221      subScopesProcessor2.Successor = mergingReducer;
222      bestSelector.Successor = rightReducer;
223      rightReducer.Successor = null;
224      mergingReducer.Successor = intCounter;
225      intCounter.Successor = comparator;
226      comparator.Successor = analyzer2;
227      analyzer2.Successor = conditionalBranch;
228      conditionalBranch.FalseBranch = selector;
229      conditionalBranch.TrueBranch = null;
230      conditionalBranch.Successor = null;
231      #endregion
232    }
233
234    public override IOperation Apply() {
235      if (CrossoverParameter.ActualValue == null)
236        return null;
237      return base.Apply();
238    }
239  }
240}
Note: See TracBrowser for help on using the repository browser.