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source: stable/HeuristicLab.Tests/HeuristicLab.Encodings.BinaryVectorEncoding-3.3/NPointCrossoverTest.cs @ 13246

Last change on this file since 13246 was 13246, checked in by ascheibe, 8 years ago

#2510 merged r13227, r13235, r13236 into stable

File size: 4.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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.Data;
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 NPointCrossoverTest {
33    /// <summary>
34    ///A test for Apply
35    ///</summary>
36    [TestMethod]
37    [TestCategory("Encodings.BinaryVector")]
38    [TestProperty("Time", "short")]
39    public void NPointCrossoverApplyTest() {
40      TestRandom random = new TestRandom();
41      BinaryVector parent1, parent2, expected, actual;
42      IntValue n;
43      bool exceptionFired;
44      // 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
45      random.Reset();
46      n = new IntValue(1);
47      random.IntNumbers = new int[] { 4 };
48      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
49      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
50      expected = new BinaryVector(new bool[] { false, false, false, false, false, false, false, false, true });
51      actual = NPointCrossover.Apply(random, parent1, parent2, n);
52      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
53
54      // 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
55      random.Reset();
56      n = new IntValue(2);
57      random.IntNumbers = new int[] { 4, 5 };
58      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
59      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
60      expected = new BinaryVector(new bool[] { false, false, false, false, false, false, false, false, false });
61      actual = NPointCrossover.Apply(random, parent1, parent2, n);
62      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
63
64      // 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
65      random.Reset();
66      n = new IntValue(2);
67      random.IntNumbers = new int[] { 4, 5 };
68      parent2 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
69      parent1 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
70      expected = new BinaryVector(new bool[] { true, true, false, true, true, false, false, false, true });
71      actual = NPointCrossover.Apply(random, parent1, parent2, n);
72      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
73
74      // The following test is not based on any published examples
75      random.Reset();
76      random.IntNumbers = new int[] { 2 };
77      parent1 = new BinaryVector(new bool[] { false, true, true, false, false }); // this parent is longer
78      parent2 = new BinaryVector(new bool[] { false, true, true, false });
79      exceptionFired = false;
80      try {
81        actual = NPointCrossover.Apply(random, parent1, parent2, n);
82      }
83      catch (System.ArgumentException) {
84        exceptionFired = true;
85      }
86      Assert.IsTrue(exceptionFired);
87    }
88  }
89}
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