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source: trunk/sources/HeuristicLab.Algorithms.GeneticAlgorithm/3.3/GeneticAlgorithmMainLoop.cs @ 4900

Last change on this file since 4900 was 4722, checked in by swagner, 14 years ago

Merged cloning refactoring branch back into trunk (#922)

File size: 10.8 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.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    private ScopeParameter CurrentScopeParameter {
76      get { return (ScopeParameter)Parameters["CurrentScope"]; }
77    }
78
79    public IScope CurrentScope {
80      get { return CurrentScopeParameter.ActualValue; }
81    }
82    #endregion
83
84    [StorableConstructor]
85    private GeneticAlgorithmMainLoop(bool deserializing) : base(deserializing) { }
86    private GeneticAlgorithmMainLoop(GeneticAlgorithmMainLoop original, Cloner cloner)
87      : base(original, cloner) {
88    }
89    public override IDeepCloneable Clone(Cloner cloner) {
90      return new GeneticAlgorithmMainLoop(this, cloner);
91    }
92    public GeneticAlgorithmMainLoop()
93      : base() {
94      Initialize();
95    }
96
97    private void Initialize() {
98      #region Create parameters
99      Parameters.Add(new ValueLookupParameter<IRandom>("Random", "A pseudo random number generator."));
100      Parameters.Add(new ValueLookupParameter<BoolValue>("Maximization", "True if the problem is a maximization problem, otherwise false."));
101      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>("Quality", "The value which represents the quality of a solution."));
102      Parameters.Add(new ValueLookupParameter<IOperator>("Selector", "The operator used to select solutions for reproduction."));
103      Parameters.Add(new ValueLookupParameter<IOperator>("Crossover", "The operator used to cross solutions."));
104      Parameters.Add(new ValueLookupParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution."));
105      Parameters.Add(new ValueLookupParameter<IOperator>("Mutator", "The operator used to mutate solutions."));
106      Parameters.Add(new ValueLookupParameter<IOperator>("Evaluator", "The operator used to evaluate solutions."));
107      Parameters.Add(new ValueLookupParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation."));
108      Parameters.Add(new ValueLookupParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed."));
109      Parameters.Add(new ValueLookupParameter<VariableCollection>("Results", "The variable collection where results should be stored."));
110      Parameters.Add(new ValueLookupParameter<IOperator>("Analyzer", "The operator used to analyze each generation."));
111      Parameters.Add(new ScopeParameter("CurrentScope", "The current scope which represents a population of solutions on which the genetic algorithm should be applied."));
112      #endregion
113
114      #region Create operators
115      VariableCreator variableCreator = new VariableCreator();
116      ResultsCollector resultsCollector1 = new ResultsCollector();
117      Placeholder analyzer1 = new Placeholder();
118      Placeholder selector = new Placeholder();
119      SubScopesProcessor subScopesProcessor1 = new SubScopesProcessor();
120      ChildrenCreator childrenCreator = new ChildrenCreator();
121      UniformSubScopesProcessor uniformSubScopesProcessor = new UniformSubScopesProcessor();
122      Placeholder crossover = new Placeholder();
123      StochasticBranch stochasticBranch = new StochasticBranch();
124      Placeholder mutator = new Placeholder();
125      Placeholder evaluator = new Placeholder();
126      SubScopesRemover subScopesRemover = new SubScopesRemover();
127      SubScopesProcessor subScopesProcessor2 = new SubScopesProcessor();
128      BestSelector bestSelector = new BestSelector();
129      RightReducer rightReducer = new RightReducer();
130      MergingReducer mergingReducer = new MergingReducer();
131      IntCounter intCounter = new IntCounter();
132      Comparator comparator = new Comparator();
133      ResultsCollector resultsCollector2 = new ResultsCollector();
134      Placeholder analyzer2 = new Placeholder();
135      ConditionalBranch conditionalBranch = new ConditionalBranch();
136
137      variableCreator.CollectedValues.Add(new ValueParameter<IntValue>("Generations", new IntValue(0))); // Class GeneticAlgorithm expects this to be called Generations
138
139      resultsCollector1.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
140      resultsCollector1.ResultsParameter.ActualName = "Results";
141
142      analyzer1.Name = "Analyzer";
143      analyzer1.OperatorParameter.ActualName = "Analyzer";
144
145      selector.Name = "Selector";
146      selector.OperatorParameter.ActualName = "Selector";
147
148      childrenCreator.ParentsPerChild = new IntValue(2);
149
150      crossover.Name = "Crossover";
151      crossover.OperatorParameter.ActualName = "Crossover";
152
153      stochasticBranch.ProbabilityParameter.ActualName = "MutationProbability";
154      stochasticBranch.RandomParameter.ActualName = "Random";
155
156      mutator.Name = "Mutator";
157      mutator.OperatorParameter.ActualName = "Mutator";
158
159      evaluator.Name = "Evaluator";
160      evaluator.OperatorParameter.ActualName = "Evaluator";
161
162      subScopesRemover.RemoveAllSubScopes = true;
163
164      bestSelector.CopySelected = new BoolValue(false);
165      bestSelector.MaximizationParameter.ActualName = "Maximization";
166      bestSelector.NumberOfSelectedSubScopesParameter.ActualName = "Elites";
167      bestSelector.QualityParameter.ActualName = "Quality";
168
169      intCounter.Increment = new IntValue(1);
170      intCounter.ValueParameter.ActualName = "Generations";
171
172      comparator.Comparison = new Comparison(ComparisonType.GreaterOrEqual);
173      comparator.LeftSideParameter.ActualName = "Generations";
174      comparator.ResultParameter.ActualName = "Terminate";
175      comparator.RightSideParameter.ActualName = "MaximumGenerations";
176
177      resultsCollector2.CollectedValues.Add(new LookupParameter<IntValue>("Generations"));
178      resultsCollector2.ResultsParameter.ActualName = "Results";
179
180      analyzer2.Name = "Analyzer";
181      analyzer2.OperatorParameter.ActualName = "Analyzer";
182
183      conditionalBranch.ConditionParameter.ActualName = "Terminate";
184      #endregion
185
186      #region Create operator graph
187      OperatorGraph.InitialOperator = variableCreator;
188      variableCreator.Successor = resultsCollector1;
189      resultsCollector1.Successor = analyzer1;
190      analyzer1.Successor = selector;
191      selector.Successor = subScopesProcessor1;
192      subScopesProcessor1.Operators.Add(new EmptyOperator());
193      subScopesProcessor1.Operators.Add(childrenCreator);
194      subScopesProcessor1.Successor = subScopesProcessor2;
195      childrenCreator.Successor = uniformSubScopesProcessor;
196      uniformSubScopesProcessor.Operator = crossover;
197      uniformSubScopesProcessor.Successor = null;
198      crossover.Successor = stochasticBranch;
199      stochasticBranch.FirstBranch = mutator;
200      stochasticBranch.SecondBranch = null;
201      stochasticBranch.Successor = evaluator;
202      mutator.Successor = null;
203      evaluator.Successor = subScopesRemover;
204      subScopesRemover.Successor = null;
205      subScopesProcessor2.Operators.Add(bestSelector);
206      subScopesProcessor2.Operators.Add(new EmptyOperator());
207      subScopesProcessor2.Successor = mergingReducer;
208      bestSelector.Successor = rightReducer;
209      rightReducer.Successor = null;
210      mergingReducer.Successor = intCounter;
211      intCounter.Successor = comparator;
212      comparator.Successor = resultsCollector2;
213      resultsCollector2.Successor = analyzer2;
214      analyzer2.Successor = conditionalBranch;
215      conditionalBranch.FalseBranch = selector;
216      conditionalBranch.TrueBranch = null;
217      conditionalBranch.Successor = null;
218      #endregion
219    }
220
221    public override IOperation Apply() {
222      if (CrossoverParameter.ActualValue == null)
223        return null;
224      return base.Apply();
225    }
226  }
227}
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