source: branches/2521_ProblemRefactoring/HeuristicLab.Problems.GrammaticalEvolution/3.4/ArtificialAnt/GEArtificialAntProblem.cs @ 17270

Last change on this file since 17270 was 17270, checked in by abeham, 3 years ago

#2521: worked on removing virtual from Maximization for single-objective problems

File size: 5.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 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 HEAL.Attic;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HeuristicLab.Data;
29using HeuristicLab.Encodings.IntegerVectorEncoding;
30using HeuristicLab.Optimization;
31using HeuristicLab.Parameters;
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  [StorableType("B6F0EBC4-FA3B-42E6-958F-404FA89C81FA")]
40  public sealed class GEArtificialAntProblem : SingleObjectiveProblem<IntegerVectorEncoding, IntegerVector> {
41
42    #region Parameter Properties
43    public IValueParameter<BoolMatrix> WorldParameter {
44      get { return (IValueParameter<BoolMatrix>)Parameters["World"]; }
45    }
46    public IFixedValueParameter<IntValue> MaxTimeStepsParameter {
47      get { return (IFixedValueParameter<IntValue>)Parameters["MaximumTimeSteps"]; }
48    }
49    public IValueParameter<IGenotypeToPhenotypeMapper> GenotypeToPhenotypeMapperParameter {
50      get { return (IValueParameter<IGenotypeToPhenotypeMapper>)Parameters["GenotypeToPhenotypeMapper"]; }
51    }
52    #endregion
53
54    #region Properties
55    public BoolMatrix World {
56      get { return WorldParameter.Value; }
57      set { WorldParameter.Value = value; }
58    }
59    public int MaxTimeSteps {
60      get { return MaxTimeStepsParameter.Value.Value; }
61      set { MaxTimeStepsParameter.Value.Value = value; }
62    }
63    #endregion
64
65    [StorableConstructor]
66    private GEArtificialAntProblem(StorableConstructorFlag _) : base(_) { }
67    [StorableHook(HookType.AfterDeserialization)]
68    private void AfterDeserialization() { }
69
70    [Storable]
71    // parameters of the wrapped problem cannot be changed therefore it is not strictly necessary to clone and store it
72    private readonly HeuristicLab.Problems.GeneticProgramming.ArtificialAnt.Problem wrappedAntProblem;
73
74    private GEArtificialAntProblem(GEArtificialAntProblem original, Cloner cloner)
75      : base(original, cloner) {
76      this.wrappedAntProblem = cloner.Clone(original.wrappedAntProblem);
77    }
78
79    public override IDeepCloneable Clone(Cloner cloner) {
80      return new GEArtificialAntProblem(this, cloner);
81    }
82
83    public GEArtificialAntProblem() : base(new IntegerVectorEncoding()) {
84      Maximization = true;
85      wrappedAntProblem = new HeuristicLab.Problems.GeneticProgramming.ArtificialAnt.Problem();
86      Parameters.Add(new ValueParameter<BoolMatrix>("World", "The world for the artificial ant with scattered food items.", wrappedAntProblem.World));
87      Parameters.Add(new FixedValueParameter<IntValue>("MaximumTimeSteps", "The number of time steps the artificial ant has available to collect all food items.", new IntValue(600)));
88      Parameters.Add(new ValueParameter<IGenotypeToPhenotypeMapper>("GenotypeToPhenotypeMapper", "Maps the genotype (an integer vector) to the phenotype (a symbolic expression tree).", new DepthFirstMapper()));
89
90      Encoding.Length = 30;
91      Encoding.Bounds = new IntMatrix(new int[,] { { 0, 100 } });
92
93      BestKnownQuality = wrappedAntProblem.BestKnownQuality;
94    }
95
96    private readonly object syncRoot = new object();
97    public override double Evaluate(IntegerVector solution, IRandom random) {
98      var bounds = Encoding.Bounds;
99      var len = Encoding.Length;
100      var grammar = wrappedAntProblem.Encoding.Grammar;
101      var mapper = GenotypeToPhenotypeMapperParameter.Value;
102
103      // Evaluate might be called concurrently therefore access to random has to be synchronized.
104      // However, results depend on the order of execution. Therefore, results might be different for the same seed when using the parallel engine.
105      IRandom fastRand;
106      lock (syncRoot) {
107        fastRand = new FastRandom(random.Next());
108      }
109      var tree = mapper.Map(fastRand, bounds, len, grammar, solution);
110
111      Interpreter interpreter = new Interpreter(tree, World, MaxTimeSteps);
112      interpreter.Run();
113
114      return interpreter.FoodEaten;
115    }
116
117    public override void Analyze(IntegerVector[] solutions, double[] qualities, ResultCollection results, IRandom random) {
118      var bounds = Encoding.Bounds;
119      var len = Encoding.Length;
120      var grammar = wrappedAntProblem.Encoding.Grammar;
121      var mapper = GenotypeToPhenotypeMapperParameter.Value;
122
123      var trees = solutions
124        .Select(ind => mapper.Map(random, bounds, len, grammar, ind))
125        .ToArray();
126
127      wrappedAntProblem.Analyze(trees, qualities, results, random);
128    }
129  }
130}
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