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source: trunk/sources/HeuristicLab.Algorithms.GeneticAlgorithm/3.3/GeneticAlgorithm.cs @ 3199

Last change on this file since 3199 was 3199, checked in by swagner, 14 years ago

Fixed and updated GeneticAlgorithmPerformanceTest (#945).
File size: 16.3 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 System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Operators;
28using HeuristicLab.Optimization;
29using HeuristicLab.Optimization.Operators;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
32using HeuristicLab.PluginInfrastructure;
33
34namespace HeuristicLab.Algorithms.GeneticAlgorithm {
35  /// <summary>
36  /// A genetic algorithm.
37  /// </summary>
38  [Item("Genetic Algorithm", "A genetic algorithm.")]
39  [Creatable("Algorithms")]
40  [StorableClass]
41  public sealed class GeneticAlgorithm : EngineAlgorithm {
42    #region Problem Properties
43    public override Type ProblemType {
44      get { return typeof(ISingleObjectiveProblem); }
45    }
46    public new ISingleObjectiveProblem Problem {
47      get { return (ISingleObjectiveProblem)base.Problem; }
48      set { base.Problem = value; }
49    }
50    #endregion
51
52    #region Parameter Properties
53    private ValueParameter<IntValue> SeedParameter {
54      get { return (ValueParameter<IntValue>)Parameters["Seed"]; }
55    }
56    private ValueParameter<BoolValue> SetSeedRandomlyParameter {
57      get { return (ValueParameter<BoolValue>)Parameters["SetSeedRandomly"]; }
58    }
59    private ValueParameter<IntValue> PopulationSizeParameter {
60      get { return (ValueParameter<IntValue>)Parameters["PopulationSize"]; }
61    }
62    private ConstrainedValueParameter<ISelector> SelectorParameter {
63      get { return (ConstrainedValueParameter<ISelector>)Parameters["Selector"]; }
64    }
65    private ConstrainedValueParameter<ICrossover> CrossoverParameter {
66      get { return (ConstrainedValueParameter<ICrossover>)Parameters["Crossover"]; }
67    }
68    private ValueParameter<PercentValue> MutationProbabilityParameter {
69      get { return (ValueParameter<PercentValue>)Parameters["MutationProbability"]; }
70    }
71    private OptionalConstrainedValueParameter<IManipulator> MutatorParameter {
72      get { return (OptionalConstrainedValueParameter<IManipulator>)Parameters["Mutator"]; }
73    }
74    private ValueParameter<IntValue> ElitesParameter {
75      get { return (ValueParameter<IntValue>)Parameters["Elites"]; }
76    }
77    private ValueParameter<IntValue> MaximumGenerationsParameter {
78      get { return (ValueParameter<IntValue>)Parameters["MaximumGenerations"]; }
79    }
80    #endregion
81
82    #region Properties
83    public IntValue Seed {
84      get { return SeedParameter.Value; }
85      set { SeedParameter.Value = value; }
86    }
87    public BoolValue SetSeedRandomly {
88      get { return SetSeedRandomlyParameter.Value; }
89      set { SetSeedRandomlyParameter.Value = value; }
90    }
91    public IntValue PopulationSize {
92      get { return PopulationSizeParameter.Value; }
93      set { PopulationSizeParameter.Value = value; }
94    }
95    public ISelector Selector {
96      get { return SelectorParameter.Value; }
97      set { SelectorParameter.Value = value; }
98    }
99    public ICrossover Crossover {
100      get { return CrossoverParameter.Value; }
101      set { CrossoverParameter.Value = value; }
102    }
103    public PercentValue MutationProbability {
104      get { return MutationProbabilityParameter.Value; }
105      set { MutationProbabilityParameter.Value = value; }
106    }
107    public IManipulator Mutator {
108      get { return MutatorParameter.Value; }
109      set { MutatorParameter.Value = value; }
110    }
111    public IntValue Elites {
112      get { return ElitesParameter.Value; }
113      set { ElitesParameter.Value = value; }
114    }
115    public IntValue MaximumGenerations {
116      get { return MaximumGenerationsParameter.Value; }
117      set { MaximumGenerationsParameter.Value = value; }
118    }
119    private RandomCreator RandomCreator {
120      get { return (RandomCreator)OperatorGraph.InitialOperator; }
121    }
122    private SolutionsCreator SolutionsCreator {
123      get { return (SolutionsCreator)RandomCreator.Successor; }
124    }
125    private GeneticAlgorithmMainLoop GeneticAlgorithmMainLoop {
126      get { return (GeneticAlgorithmMainLoop)SolutionsCreator.Successor; }
127    }
128    private List<ISelector> selectors;
129    private IEnumerable<ISelector> Selectors {
130      get { return selectors; }
131    }
132    #endregion
133
134    public GeneticAlgorithm()
135      : base() {
136      Parameters.Add(new ValueParameter<IntValue>("Seed", "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
137      Parameters.Add(new ValueParameter<BoolValue>("SetSeedRandomly", "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
138      Parameters.Add(new ValueParameter<IntValue>("PopulationSize", "The size of the population of solutions.", new IntValue(100)));
139      Parameters.Add(new ConstrainedValueParameter<ISelector>("Selector", "The operator used to select solutions for reproduction."));
140      Parameters.Add(new ConstrainedValueParameter<ICrossover>("Crossover", "The operator used to cross solutions."));
141      Parameters.Add(new ValueParameter<PercentValue>("MutationProbability", "The probability that the mutation operator is applied on a solution.", new PercentValue(0.05)));
142      Parameters.Add(new OptionalConstrainedValueParameter<IManipulator>("Mutator", "The operator used to mutate solutions."));
143      Parameters.Add(new ValueParameter<IntValue>("Elites", "The numer of elite solutions which are kept in each generation.", new IntValue(1)));
144      Parameters.Add(new ValueParameter<IntValue>("MaximumGenerations", "The maximum number of generations which should be processed.", new IntValue(1000)));
145
146      RandomCreator randomCreator = new RandomCreator();
147      SolutionsCreator solutionsCreator = new SolutionsCreator();
148      GeneticAlgorithmMainLoop geneticAlgorithmMainLoop = new GeneticAlgorithmMainLoop();
149      OperatorGraph.InitialOperator = randomCreator;
150
151      randomCreator.RandomParameter.ActualName = "Random";
152      randomCreator.SeedParameter.ActualName = SeedParameter.Name;
153      randomCreator.SeedParameter.Value = null;
154      randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameter.Name;
155      randomCreator.SetSeedRandomlyParameter.Value = null;
156      randomCreator.Successor = solutionsCreator;
157
158      solutionsCreator.NumberOfSolutionsParameter.ActualName = PopulationSizeParameter.Name;
159      solutionsCreator.Successor = geneticAlgorithmMainLoop;
160
161      geneticAlgorithmMainLoop.SelectorParameter.ActualName = SelectorParameter.Name;
162      geneticAlgorithmMainLoop.CrossoverParameter.ActualName = CrossoverParameter.Name;
163      geneticAlgorithmMainLoop.ElitesParameter.ActualName = ElitesParameter.Name;
164      geneticAlgorithmMainLoop.MaximumGenerationsParameter.ActualName = MaximumGenerationsParameter.Name;
165      geneticAlgorithmMainLoop.MutatorParameter.ActualName = MutatorParameter.Name;
166      geneticAlgorithmMainLoop.MutationProbabilityParameter.ActualName = MutationProbabilityParameter.Name;
167      geneticAlgorithmMainLoop.RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
168      geneticAlgorithmMainLoop.ResultsParameter.ActualName = "Results";
169
170      Initialze();
171    }
172    [StorableConstructor]
173    private GeneticAlgorithm(bool deserializing) : base() { }
174
175    public override IDeepCloneable Clone(Cloner cloner) {
176      GeneticAlgorithm clone = (GeneticAlgorithm)base.Clone(cloner);
177      clone.Initialze();
178      return clone;
179    }
180
181    protected override void OnPrepared() {
182      base.OnPrepared();
183      if (Engine != null) {
184        if (Problem == null) Engine.Prepare(null);
185      }
186    }
187
188    #region Events
189    protected override void OnProblemChanged() {
190      ParameterizeStochasticOperator(Problem.SolutionCreator);
191      ParameterizeStochasticOperator(Problem.Evaluator);
192      ParameterizeStochasticOperator(Problem.Visualizer);
193      foreach (IOperator op in Problem.Operators) ParameterizeStochasticOperator(op);
194      ParameterizeSolutionsCreator();
195      ParameterizeGeneticAlgorithmMainLoop();
196      ParameterizeSelectors();
197      UpdateCrossovers();
198      UpdateMutators();
199      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
200      if (Problem.Visualizer != null) Problem.Visualizer.VisualizationParameter.ActualNameChanged += new EventHandler(Visualizer_VisualizationParameter_ActualNameChanged);
201      base.OnProblemChanged();
202    }
203
204    protected override void Problem_SolutionCreatorChanged(object sender, EventArgs e) {
205      ParameterizeStochasticOperator(Problem.SolutionCreator);
206      ParameterizeSolutionsCreator();
207      base.Problem_SolutionCreatorChanged(sender, e);
208    }
209    protected override void Problem_EvaluatorChanged(object sender, EventArgs e) {
210      ParameterizeStochasticOperator(Problem.Evaluator);
211      ParameterizeSolutionsCreator();
212      ParameterizeGeneticAlgorithmMainLoop();
213      ParameterizeSelectors();
214      Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
215      base.Problem_EvaluatorChanged(sender, e);
216    }
217    protected override void Problem_VisualizerChanged(object sender, EventArgs e) {
218      ParameterizeStochasticOperator(Problem.Visualizer);
219      ParameterizeGeneticAlgorithmMainLoop();
220      if (Problem.Visualizer != null) Problem.Visualizer.VisualizationParameter.ActualNameChanged += new EventHandler(Visualizer_VisualizationParameter_ActualNameChanged);
221      base.Problem_VisualizerChanged(sender, e);
222    }
223    protected override void Problem_OperatorsChanged(object sender, EventArgs e) {
224      foreach (IOperator op in Problem.Operators) ParameterizeStochasticOperator(op);
225      UpdateCrossovers();
226      UpdateMutators();
227      base.Problem_OperatorsChanged(sender, e);
228    }
229    private void ElitesParameter_ValueChanged(object sender, EventArgs e) {
230      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
231      ParameterizeSelectors();
232    }
233    private void Elites_ValueChanged(object sender, EventArgs e) {
234      ParameterizeSelectors();
235    }
236    private void PopulationSizeParameter_ValueChanged(object sender, EventArgs e) {
237      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
238      ParameterizeSelectors();
239    }
240    private void PopulationSize_ValueChanged(object sender, EventArgs e) {
241      ParameterizeSelectors();
242    }
243    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
244      ParameterizeGeneticAlgorithmMainLoop();
245      ParameterizeSelectors();
246    }
247    private void Visualizer_VisualizationParameter_ActualNameChanged(object sender, EventArgs e) {
248      ParameterizeGeneticAlgorithmMainLoop();
249    }
250    #endregion
251
252    #region Helpers
253    [StorableHook(HookType.AfterDeserialization)]
254    private void Initialze() {
255      InitializeSelectors();
256      UpdateSelectors();
257      PopulationSizeParameter.ValueChanged += new EventHandler(PopulationSizeParameter_ValueChanged);
258      PopulationSize.ValueChanged += new EventHandler(PopulationSize_ValueChanged);
259      ElitesParameter.ValueChanged += new EventHandler(ElitesParameter_ValueChanged);
260      Elites.ValueChanged += new EventHandler(Elites_ValueChanged);
261      if (Problem != null) {
262        UpdateCrossovers();
263        UpdateMutators();
264        Problem.Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
265        if (Problem.Visualizer != null) Problem.Visualizer.VisualizationParameter.ActualNameChanged += new EventHandler(Visualizer_VisualizationParameter_ActualNameChanged);
266      }
267    }
268
269    private void ParameterizeSolutionsCreator() {
270      SolutionsCreator.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
271      SolutionsCreator.SolutionCreatorParameter.ActualName = Problem.SolutionCreatorParameter.Name;
272    }
273    private void ParameterizeGeneticAlgorithmMainLoop() {
274      GeneticAlgorithmMainLoop.BestKnownQualityParameter.ActualName = Problem.BestKnownQualityParameter.Name;
275      GeneticAlgorithmMainLoop.EvaluatorParameter.ActualName = Problem.EvaluatorParameter.Name;
276      GeneticAlgorithmMainLoop.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
277      GeneticAlgorithmMainLoop.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
278      GeneticAlgorithmMainLoop.VisualizerParameter.ActualName = Problem.VisualizerParameter.Name;
279      if (Problem.Visualizer != null)
280        GeneticAlgorithmMainLoop.VisualizationParameter.ActualName = Problem.Visualizer.VisualizationParameter.ActualName;
281    }
282    private void ParameterizeStochasticOperator(IOperator op) {
283      if (op is IStochasticOperator)
284        ((IStochasticOperator)op).RandomParameter.ActualName = RandomCreator.RandomParameter.ActualName;
285    }
286    private void InitializeSelectors() {
287      selectors = new List<ISelector>();
288      if (ApplicationManager.Manager != null) selectors.AddRange(ApplicationManager.Manager.GetInstances<ISelector>().Where(x => !(x is IMultiObjectiveSelector)).OrderBy(x => x.Name));
289      ParameterizeSelectors();
290    }
291    private void ParameterizeSelectors() {
292      foreach (ISelector selector in Selectors) {
293        selector.CopySelected = new BoolValue(true);
294        selector.NumberOfSelectedSubScopesParameter.Value = new IntValue(2 * (PopulationSizeParameter.Value.Value - ElitesParameter.Value.Value));
295        ParameterizeStochasticOperator(selector);
296      }
297      if (Problem != null) {
298        foreach (ISingleObjectiveSelector selector in Selectors.OfType<ISingleObjectiveSelector>()) {
299          selector.MaximizationParameter.ActualName = Problem.MaximizationParameter.Name;
300          selector.QualityParameter.ActualName = Problem.Evaluator.QualityParameter.ActualName;
301        }
302      }
303    }
304    private void UpdateSelectors() {
305      if (ApplicationManager.Manager != null) {
306        ISelector oldSelector = SelectorParameter.Value;
307        SelectorParameter.ValidValues.Clear();
308        foreach (ISelector selector in Selectors.OrderBy(x => x.Name))
309          SelectorParameter.ValidValues.Add(selector);
310        if (oldSelector != null) {
311          ISelector selector = SelectorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldSelector.GetType());
312          if (selector != null) SelectorParameter.Value = selector;
313        }
314      }
315    }
316    private void UpdateCrossovers() {
317      if (ApplicationManager.Manager != null) {
318        ICrossover oldCrossover = CrossoverParameter.Value;
319        CrossoverParameter.ValidValues.Clear();
320        foreach (ICrossover crossover in Problem.Operators.OfType<ICrossover>().OrderBy(x => x.Name))
321          CrossoverParameter.ValidValues.Add(crossover);
322        if (oldCrossover != null) {
323          ICrossover crossover = CrossoverParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldCrossover.GetType());
324          if (crossover != null) CrossoverParameter.Value = crossover;
325        }
326      }
327    }
328    private void UpdateMutators() {
329      if (ApplicationManager.Manager != null) {
330        IManipulator oldMutator = MutatorParameter.Value;
331        MutatorParameter.ValidValues.Clear();
332        foreach (IManipulator mutator in Problem.Operators.OfType<IManipulator>().OrderBy(x => x.Name))
333          MutatorParameter.ValidValues.Add(mutator);
334        if (oldMutator != null) {
335          IManipulator mutator = MutatorParameter.ValidValues.FirstOrDefault(x => x.GetType() == oldMutator.GetType());
336          if (mutator != null) MutatorParameter.Value = mutator;
337        }
338      }
339    }
340    #endregion
341  }
342}
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