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source: branches/HLScript/HeuristicLab.Tests/HeuristicLab.Encodings.BinaryVectorEncoding-3.3/UniformCrossoverTest.cs @ 10632

Last change on this file since 10632 was 9777, checked in by abeham, 11 years ago

#2088: Added test attributes and cleaned up tests of the encodings

File size: 5.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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 HeuristicLab.Core;
23using HeuristicLab.Tests;
24using Microsoft.VisualStudio.TestTools.UnitTesting;
25
26namespace HeuristicLab.Encodings.BinaryVectorEncoding.Tests {
27  /// <summary>
28  ///This is a test class for SinglePointCrossoverTest and is intended
29  ///to contain all SinglePointCrossoverTest Unit Tests
30  ///</summary>
31  [TestClass()]
32  public class UniformCrossoverTest {
33    /// <summary>
34    ///A test for Cross
35    ///</summary>
36    [TestMethod]
37    [TestCategory("Encodings.BinaryVector")]
38    [TestProperty("Time", "short")]
39    public void SinglePointCrossoverCrossTest() {
40      UniformCrossover_Accessor target = new UniformCrossover_Accessor(new PrivateObject(typeof(UniformCrossover)));
41      ItemArray<BinaryVector> parents;
42      TestRandom random = new TestRandom();
43      bool exceptionFired;
44      // The following test checks if there is an exception when there are more than 2 parents
45      random.Reset();
46      parents = new ItemArray<BinaryVector>(new BinaryVector[] { new BinaryVector(5), new BinaryVector(6), new BinaryVector(4) });
47      exceptionFired = false;
48      try {
49        BinaryVector actual;
50        actual = target.Cross(random, parents);
51      } catch (System.ArgumentException) {
52        exceptionFired = true;
53      }
54      Assert.IsTrue(exceptionFired);
55      // The following test checks if there is an exception when there are less than 2 parents
56      random.Reset();
57      parents = new ItemArray<BinaryVector>(new BinaryVector[] { new BinaryVector(4) });
58      exceptionFired = false;
59      try {
60        BinaryVector actual;
61        actual = target.Cross(random, parents);
62      } catch (System.ArgumentException) {
63        exceptionFired = true;
64      }
65      Assert.IsTrue(exceptionFired);
66    }
67
68    /// <summary>
69    ///A test for Apply
70    ///</summary>
71    [TestMethod]
72    [TestCategory("Encodings.BinaryVector")]
73    [TestProperty("Time", "short")]
74    public void SinglePointCrossoverApplyTest() {
75      TestRandom random = new TestRandom();
76      BinaryVector parent1, parent2, expected, actual;
77      bool exceptionFired;
78      // 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. 49
79      random.Reset();
80      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
81      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
82      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
83      expected = new BinaryVector(new bool[] { false, true, false, false, false, false, false, false, false });
84      actual = UniformCrossover.Apply(random, parent1, parent2);
85      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
86
87      // 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. 49
88      random.Reset();
89      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
90      parent2 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
91      parent1 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
92      expected = new BinaryVector(new bool[] { true, false, false, true, true, false, false, false, true });
93      actual = UniformCrossover.Apply(random, parent1, parent2);
94      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
95
96      // The following test is not based on any published examples
97      random.Reset();
98      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
99      parent1 = new BinaryVector(new bool[] { false, true, true, false, false }); // this parent is longer
100      parent2 = new BinaryVector(new bool[] { false, true, true, false });
101      exceptionFired = false;
102      try {
103        actual = UniformCrossover.Apply(random, parent1, parent2);
104      } catch (System.ArgumentException) {
105        exceptionFired = true;
106      }
107      Assert.IsTrue(exceptionFired);
108    }
109  }
110}
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