source: trunk/sources/HeuristicLab.Problems.GrammaticalEvolution/3.4/ArtificialAnt/GEArtificialAntProblem.cs @ 13243

Last change on this file since 13243 was 13243, checked in by mkommend, 4 years ago

#2512: Introduced syncRoot for synchronizing access to the random object.

File size: 5.8 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 * Author: Sabine Winkler
21 */
22#endregion
23
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.IntegerVectorEncoding;
29using HeuristicLab.Optimization;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
32using HeuristicLab.Problems.GeneticProgramming.ArtificialAnt;
33using HeuristicLab.Problems.GrammaticalEvolution.Mappers;
34using HeuristicLab.Random;
35
36namespace HeuristicLab.Problems.GrammaticalEvolution {
37  [Item("Grammatical Evolution Artificial Ant Problem (GE)", "Represents the Artificial Ant problem, implemented in Grammatical Evolution.")]
38  [Creatable(CreatableAttribute.Categories.GeneticProgrammingProblems, Priority = 170)]
39  [StorableClass]
40  public sealed class GEArtificialAntProblem : SingleObjectiveBasicProblem<IntegerVectorEncoding>, IStorableContent {
41    public string Filename { get; set; }
42
43    #region Parameter Properties
44    public IValueParameter<BoolMatrix> WorldParameter {
45      get { return (IValueParameter<BoolMatrix>)Parameters["World"]; }
46    }
47    public IFixedValueParameter<IntValue> MaxTimeStepsParameter {
48      get { return (IFixedValueParameter<IntValue>)Parameters["MaximumTimeSteps"]; }
49    }
50    public IValueParameter<IGenotypeToPhenotypeMapper> GenotypeToPhenotypeMapperParameter {
51      get { return (IValueParameter<IGenotypeToPhenotypeMapper>)Parameters["GenotypeToPhenotypeMapper"]; }
52    }
53    #endregion
54
55    #region Properties
56    public BoolMatrix World {
57      get { return WorldParameter.Value; }
58      set { WorldParameter.Value = value; }
59    }
60    public int MaxTimeSteps {
61      get { return MaxTimeStepsParameter.Value.Value; }
62      set { MaxTimeStepsParameter.Value.Value = value; }
63    }
64    #endregion
65
66    [StorableConstructor]
67    private GEArtificialAntProblem(bool deserializing) : base(deserializing) { }
68    [StorableHook(HookType.AfterDeserialization)]
69    private void AfterDeserialization() { }
70
71    public override bool Maximization {
72      get { return true; }
73    }
74
75    [Storable]
76    // parameters of the wrapped problem cannot be changed therefore it is not strictly necessary to clone and store it
77    private readonly HeuristicLab.Problems.GeneticProgramming.ArtificialAnt.Problem wrappedAntProblem;
78
79    private GEArtificialAntProblem(GEArtificialAntProblem original, Cloner cloner)
80      : base(original, cloner) {
81      this.wrappedAntProblem = cloner.Clone(original.wrappedAntProblem);
82    }
83
84    public override IDeepCloneable Clone(Cloner cloner) {
85      return new GEArtificialAntProblem(this, cloner);
86    }
87
88    public GEArtificialAntProblem()
89      : base() {
90      wrappedAntProblem = new HeuristicLab.Problems.GeneticProgramming.ArtificialAnt.Problem();
91      Parameters.Add(new ValueParameter<BoolMatrix>("World", "The world for the artificial ant with scattered food items.", wrappedAntProblem.World));
92      Parameters.Add(new FixedValueParameter<IntValue>("MaximumTimeSteps", "The number of time steps the artificial ant has available to collect all food items.", new IntValue(600)));
93      Parameters.Add(new ValueParameter<IGenotypeToPhenotypeMapper>("GenotypeToPhenotypeMapper", "Maps the genotype (an integer vector) to the phenotype (a symbolic expression tree).", new DepthFirstMapper()));
94
95      Encoding = new IntegerVectorEncoding(30) { Bounds = new IntMatrix(new int[,] { { 0, 100 } }) };
96
97      BestKnownQuality = wrappedAntProblem.BestKnownQuality;
98    }
99
100    private readonly object syncRoot = new object();
101    public override double Evaluate(Individual individual, IRandom random) {
102      var vector = individual.IntegerVector();
103
104      var bounds = Encoding.Bounds;
105      var len = Encoding.Length;
106      var grammar = wrappedAntProblem.Encoding.Grammar;
107      var mapper = GenotypeToPhenotypeMapperParameter.Value;
108
109      // Evaluate might be called concurrently therefore access to random has to be synchronized.
110      // However, results depend on the order of execution. Therefore, results might be different for the same seed when using the parallel engine.
111      IRandom fastRand;
112      lock (syncRoot) {
113        fastRand = new FastRandom(random.Next());
114      }
115      var tree = mapper.Map(fastRand, bounds, len, grammar, vector);
116
117      Interpreter interpreter = new Interpreter(tree, World, MaxTimeSteps);
118      interpreter.Run();
119
120      return interpreter.FoodEaten;
121    }
122
123    public override void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
124      var bounds = Encoding.Bounds;
125      var len = Encoding.Length;
126      var grammar = wrappedAntProblem.Encoding.Grammar;
127      var mapper = GenotypeToPhenotypeMapperParameter.Value;
128
129      var trees = individuals
130        .Select(ind => mapper.Map(random, bounds, len, grammar, ind.IntegerVector()))
131        .ToArray();
132
133      wrappedAntProblem.Analyze(trees, qualities, results, random);
134    }
135  }
136}
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