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source: stable/HeuristicLab.Tests/HeuristicLab-3.3/Samples/GAGroupingProblemSampleTest.cs @ 15762

Last change on this file since 15762 was 15584, checked in by swagner, 7 years ago

#2640: Updated year of copyrights in license headers on stable

File size: 5.2 KB
RevLine 
[11450]1#region License Information
2/* HeuristicLab
[15584]3 * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[11450]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.IO;
23using HeuristicLab.Algorithms.GeneticAlgorithm;
[12743]24using HeuristicLab.Encodings.LinearLinkageEncoding;
[11450]25using HeuristicLab.Persistence.Default.Xml;
[12743]26using HeuristicLab.Problems.Programmable;
[11450]27using HeuristicLab.Selection;
28using Microsoft.VisualStudio.TestTools.UnitTesting;
29
30namespace HeuristicLab.Tests {
31  [TestClass]
[12743]32  public class GAGroupingProblemSampleTest {
33    private const string SampleFileName = "GA_Grouping";
34    #region Code
35    private const string ProblemCode = @"
36using System;
37using System.Linq;
38using System.Collections.Generic;
39using HeuristicLab.Common;
40using HeuristicLab.Core;
41using HeuristicLab.Data;
42using HeuristicLab.Encodings.LinearLinkageEncoding;
43using HeuristicLab.Optimization;
44using HeuristicLab.Problems.Programmable;
[11450]45
[12743]46namespace HeuristicLab.Problems.Programmable {
47  public class CompiledSingleObjectiveProblemDefinition : CompiledProblemDefinition, ISingleObjectiveProblemDefinition {
48    private const int ProblemSize = 100;
49    public bool Maximization { get { return false; } }
50
[15217]51    private bool[,] adjacencyMatrix;
[12743]52     
53    public override void Initialize() {
54      var encoding = new LinearLinkageEncoding(""lle"", length: ProblemSize);
[15217]55      adjacencyMatrix = new bool[encoding.Length, encoding.Length];
[12743]56      var random = new System.Random(13);
57      for (var i = 0; i < encoding.Length - 1; i++)
58        for (var j = i + 1; j < encoding.Length; j++)
[15217]59          adjacencyMatrix[i, j] = adjacencyMatrix[j, i] = random.Next(2) == 0;
[12743]60     
61      Encoding = encoding;
62    }
63
64    public double Evaluate(Individual individual, IRandom random) {
65      var penalty = 0;
66      var groups = individual.LinearLinkage(""lle"").GetGroups().ToList();
67      for (var i = 0; i < groups.Count; i++) {
68        for (var j = 0; j < groups[i].Count; j++)
69          for (var k = j + 1; k < groups[i].Count; k++)
[15217]70            if (!adjacencyMatrix[groups[i][j], groups[i][k]]) penalty++;
[12743]71      }
[15217]72      var result = groups.Count;
73      if (penalty > 0) result += penalty + ProblemSize;
74      return result;
[12743]75    }
76
77    public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) { }
78
79    public IEnumerable<Individual> GetNeighbors(Individual individual, IRandom random) {
80      foreach (var move in ExhaustiveSwap2MoveGenerator.Generate(individual.LinearLinkage(""lle""))) {
81        var neighbor = individual.Copy();
82        var lle = neighbor.LinearLinkage(""lle"");
83        Swap2MoveMaker.Apply(lle, move);
84        yield return neighbor;
85      }
86    }
87  }
88}
89";
90    #endregion
91
[11450]92    [TestMethod]
93    [TestCategory("Samples.Create")]
94    [TestProperty("Time", "medium")]
[12743]95    public void CreateGaGroupingProblemSampleTest() {
96      var ga = CreateGaGroupingProblemSample();
[11514]97      string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
98      XmlGenerator.Serialize(ga, path);
[11450]99    }
[11514]100
[11450]101    [TestMethod]
102    [TestCategory("Samples.Execute")]
103    [TestProperty("Time", "long")]
[12743]104    public void RunGaGroupingProblemSampleTest() {
105      var ga = CreateGaGroupingProblemSample();
[11450]106      ga.SetSeedRandomly.Value = false;
107      SamplesUtils.RunAlgorithm(ga);
[15217]108      Assert.AreEqual(127, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
109      Assert.AreEqual(129,38, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
110      Assert.AreEqual(132, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
[11450]111      Assert.AreEqual(99100, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
112    }
113
[12743]114    private GeneticAlgorithm CreateGaGroupingProblemSample() {
[11450]115      GeneticAlgorithm ga = new GeneticAlgorithm();
[11514]116
[11450]117      #region Problem Configuration
[12743]118      var problem = new SingleObjectiveProgrammableProblem() {
119        ProblemScript = { Code = ProblemCode }
120      };
121      problem.ProblemScript.Compile();
[11450]122      #endregion
123      #region Algorithm Configuration
[15217]124      ga.Name = "Genetic Algorithm - Graph Coloring";
125      ga.Description = "A genetic algorithm which solves a graph coloring problem using the linear linkage encoding.";
[12743]126      ga.Problem = problem;
127      SamplesUtils.ConfigureGeneticAlgorithmParameters<TournamentSelector, MultiLinearLinkageCrossover, MultiLinearLinkageManipulator>(
128        ga, 100, 1, 1000, 0.05, 2);
[11450]129      #endregion
[11514]130
[11450]131      return ga;
132    }
133  }
134}
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