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source: branches/2839_HiveProjectManagement/HeuristicLab.Tests/HeuristicLab.Encodings.BinaryVectorEncoding-3.3/UniformCrossoverTest.cs @ 16057

Last change on this file since 16057 was 16057, checked in by jkarder, 6 years ago

#2839:

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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2018 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.Tests;
23using Microsoft.VisualStudio.TestTools.UnitTesting;
24
25namespace HeuristicLab.Encodings.BinaryVectorEncoding.Tests {
26  /// <summary>
27  ///This is a test class for SinglePointCrossoverTest and is intended
28  ///to contain all SinglePointCrossoverTest Unit Tests
29  ///</summary>
30  [TestClass()]
31  public class UniformCrossoverTest {
32    /// <summary>
33    ///A test for Apply
34    ///</summary>
35    [TestMethod]
36    [TestCategory("Encodings.BinaryVector")]
37    [TestProperty("Time", "short")]
38    public void SinglePointCrossoverApplyTest() {
39      TestRandom random = new TestRandom();
40      BinaryVector parent1, parent2, expected, actual;
41      bool exceptionFired;
42      // 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
43      random.Reset();
44      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
45      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
46      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
47      expected = new BinaryVector(new bool[] { false, true, false, false, false, false, false, false, false });
48      actual = UniformCrossover.Apply(random, parent1, parent2);
49      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
50
51      // 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
52      random.Reset();
53      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
54      parent2 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
55      parent1 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
56      expected = new BinaryVector(new bool[] { true, false, false, true, true, false, false, false, true });
57      actual = UniformCrossover.Apply(random, parent1, parent2);
58      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
59
60      // The following test is not based on any published examples
61      random.Reset();
62      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
63      parent1 = new BinaryVector(new bool[] { false, true, true, false, false }); // this parent is longer
64      parent2 = new BinaryVector(new bool[] { false, true, true, false });
65      exceptionFired = false;
66      try {
67        actual = UniformCrossover.Apply(random, parent1, parent2);
68      }
69      catch (System.ArgumentException) {
70        exceptionFired = true;
71      }
72      Assert.IsTrue(exceptionFired);
73    }
74  }
75}
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