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source: trunk/sources/HeuristicLab.Tests/HeuristicLab.Encodings.BinaryVectorEncoding-3.3/NPointCrossoverTest.cs @ 9522

Last change on this file since 9522 was 9456, checked in by swagner, 12 years ago

Updated copyright year and added some missing license headers (#1889)

File size: 7.1 KB
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[3742]1#region License Information
2/* HeuristicLab
[9456]3 * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[3742]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
[4068]22using HeuristicLab.Core;
23using HeuristicLab.Data;
[3742]24using HeuristicLab.Encodings.BinaryVectorEncoding;
[6891]25using HeuristicLab.Tests;
[3062]26using Microsoft.VisualStudio.TestTools.UnitTesting;
27
28namespace HeuristicLab.Encodings.BinaryVectorEncoding_33.Tests {
29
30
31  /// <summary>
32  ///This is a test class for SinglePointCrossoverTest and is intended
33  ///to contain all SinglePointCrossoverTest Unit Tests
34  ///</summary>
35  [TestClass()]
36  public class NPointCrossoverTest {
37
38
39    private TestContext testContextInstance;
40
41    /// <summary>
42    ///Gets or sets the test context which provides
43    ///information about and functionality for the current test run.
44    ///</summary>
45    public TestContext TestContext {
46      get {
47        return testContextInstance;
48      }
49      set {
50        testContextInstance = value;
51      }
52    }
53
54    #region Additional test attributes
55    //
56    //You can use the following additional attributes as you write your tests:
57    //
58    //Use ClassInitialize to run code before running the first test in the class
59    //[ClassInitialize()]
60    //public static void MyClassInitialize(TestContext testContext)
61    //{
62    //}
63    //
64    //Use ClassCleanup to run code after all tests in a class have run
65    //[ClassCleanup()]
66    //public static void MyClassCleanup()
67    //{
68    //}
69    //
70    //Use TestInitialize to run code before running each test
71    //[TestInitialize()]
72    //public void MyTestInitialize()
73    //{
74    //}
75    //
76    //Use TestCleanup to run code after each test has run
77    //[TestCleanup()]
78    //public void MyTestCleanup()
79    //{
80    //}
81    //
82    #endregion
83
84    /// <summary>
85    ///A test for Cross
86    ///</summary>
87    [TestMethod()]
88    [DeploymentItem("HeuristicLab.Encodings.BinaryVectorEncoding-3.3.dll")]
[7932]89    public void NPointCrossoverCrossTest() {
[3062]90      NPointCrossover_Accessor target = new NPointCrossover_Accessor(new PrivateObject(typeof(NPointCrossover)));
91      ItemArray<BinaryVector> parents;
92      TestRandom random = new TestRandom();
93      bool exceptionFired;
94      // The following test checks if there is an exception when there are more than 2 parents
95      random.Reset();
96      parents = new ItemArray<BinaryVector>(new BinaryVector[] { new BinaryVector(5), new BinaryVector(6), new BinaryVector(4) });
97      exceptionFired = false;
98      try {
99        BinaryVector actual;
100        actual = target.Cross(random, parents);
101      }
102      catch (System.ArgumentException) {
103        exceptionFired = true;
104      }
105      Assert.IsTrue(exceptionFired);
106      // The following test checks if there is an exception when there are less than 2 parents
107      random.Reset();
108      parents = new ItemArray<BinaryVector>(new BinaryVector[] { new BinaryVector(4) });
109      exceptionFired = false;
110      try {
111        BinaryVector actual;
112        actual = target.Cross(random, parents);
[4068]113      }
114      catch (System.ArgumentException) {
[3062]115        exceptionFired = true;
116      }
117      Assert.IsTrue(exceptionFired);
118    }
119
120    /// <summary>
121    ///A test for Apply
122    ///</summary>
123    [TestMethod()]
[7932]124    public void NPointCrossoverApplyTest() {
[3062]125      TestRandom random = new TestRandom();
126      BinaryVector parent1, parent2, expected, actual;
127      IntValue n;
128      bool exceptionFired;
129      // The following test is based on Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg, p. 48
130      random.Reset();
131      n = new IntValue(1);
132      random.IntNumbers = new int[] { 4 };
133      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
134      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
135      expected = new BinaryVector(new bool[] { false, false, false, false, false, false, false, false, true });
136      actual = NPointCrossover.Apply(random, parent1, parent2, n);
137      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
[4068]138
[3062]139      // The following test is based on Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg, p. 48
140      random.Reset();
141      n = new IntValue(2);
142      random.IntNumbers = new int[] { 4, 5 };
143      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
144      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
145      expected = new BinaryVector(new bool[] { false, false, false, false, false, false, false, false, false });
146      actual = NPointCrossover.Apply(random, parent1, parent2, n);
147      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
148
149      // The following test is based on Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg, p. 48
150      random.Reset();
151      n = new IntValue(2);
152      random.IntNumbers = new int[] { 4, 5 };
153      parent2 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
154      parent1 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
155      expected = new BinaryVector(new bool[] { true, true, false, true, true, false, false, false, true });
156      actual = NPointCrossover.Apply(random, parent1, parent2, n);
157      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
158
159      // The following test is not based on any published examples
160      random.Reset();
161      random.IntNumbers = new int[] { 2 };
162      parent1 = new BinaryVector(new bool[] { false, true, true, false, false }); // this parent is longer
163      parent2 = new BinaryVector(new bool[] { false, true, true, false });
164      exceptionFired = false;
165      try {
166        actual = NPointCrossover.Apply(random, parent1, parent2, n);
[4068]167      }
168      catch (System.ArgumentException) {
[3062]169        exceptionFired = true;
170      }
171      Assert.IsTrue(exceptionFired);
172    }
173
174    /// <summary>
175    ///A test for SinglePointCrossover Constructor
176    ///</summary>
177    [TestMethod()]
[7932]178    public void NPointCrossoverConstructorTest() {
[3062]179      NPointCrossover target = new NPointCrossover();
180    }
181  }
182}
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