source: trunk/sources/HeuristicLab.Encodings.BinaryVectorEncoding/3.3/Tests/UniformCrossoverTest.cs @ 3538

using HeuristicLab.Encodings.BinaryVectorEncoding;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Parameters;

namespace HeuristicLab.Encodings.BinaryVectorEncoding_33.Tests {


  /// <summary>
  ///This is a test class for SinglePointCrossoverTest and is intended
  ///to contain all SinglePointCrossoverTest Unit Tests
  ///</summary>
  [TestClass()]
  public class UniformCrossoverTest {


    private TestContext testContextInstance;

    /// <summary>
    ///Gets or sets the test context which provides
    ///information about and functionality for the current test run.
    ///</summary>
    public TestContext TestContext {
      get {
        return testContextInstance;
      }
      set {
        testContextInstance = value;
      }
    }

    #region Additional test attributes
    // 
    //You can use the following additional attributes as you write your tests:
    //
    //Use ClassInitialize to run code before running the first test in the class
    //[ClassInitialize()]
    //public static void MyClassInitialize(TestContext testContext)
    //{
    //}
    //
    //Use ClassCleanup to run code after all tests in a class have run
    //[ClassCleanup()]
    //public static void MyClassCleanup()
    //{
    //}
    //
    //Use TestInitialize to run code before running each test
    //[TestInitialize()]
    //public void MyTestInitialize()
    //{
    //}
    //
    //Use TestCleanup to run code after each test has run
    //[TestCleanup()]
    //public void MyTestCleanup()
    //{
    //}
    //
    #endregion

    /// <summary>
    ///A test for Cross
    ///</summary>
    [TestMethod()]
    [DeploymentItem("HeuristicLab.Encodings.BinaryVectorEncoding-3.3.dll")]
    public void SinglePointCrossoverCrossTest() {
      UniformCrossover_Accessor target = new UniformCrossover_Accessor(new PrivateObject(typeof(UniformCrossover)));
      ItemArray<BinaryVector> parents;
      TestRandom random = new TestRandom();
      bool exceptionFired;
      // The following test checks if there is an exception when there are more than 2 parents
      random.Reset();
      parents = new ItemArray<BinaryVector>(new BinaryVector[] { new BinaryVector(5), new BinaryVector(6), new BinaryVector(4) });
      exceptionFired = false;
      try {
        BinaryVector actual;
        actual = target.Cross(random, parents);
      }
      catch (System.ArgumentException) {
        exceptionFired = true;
      }
      Assert.IsTrue(exceptionFired);
      // The following test checks if there is an exception when there are less than 2 parents
      random.Reset();
      parents = new ItemArray<BinaryVector>(new BinaryVector[] { new BinaryVector(4) });
      exceptionFired = false;
      try {
        BinaryVector actual;
        actual = target.Cross(random, parents);
      } catch (System.ArgumentException) {
        exceptionFired = true;
      }
      Assert.IsTrue(exceptionFired);
    }

    /// <summary>
    ///A test for Apply
    ///</summary>
    [TestMethod()]
    public void SinglePointCrossoverApplyTest() {
      TestRandom random = new TestRandom();
      BinaryVector parent1, parent2, expected, actual;
      bool exceptionFired;
      // 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
      random.Reset();
      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
      parent1 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
      parent2 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
      expected = new BinaryVector(new bool[] { false, true, false, false, false, false, false, false, false});
      actual = UniformCrossover.Apply(random, parent1, parent2);
      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));

      // 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
      random.Reset();
      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
      parent2 = new BinaryVector(new bool[] { false, false, false, false, true, false, false, false, false });
      parent1 = new BinaryVector(new bool[] { true, true, false, true, false, false, false, false, true });
      expected = new BinaryVector(new bool[] { true, false, false, true, true, false, false, false, true });
      actual = UniformCrossover.Apply(random, parent1, parent2);
      Assert.IsTrue(Auxiliary.BinaryVectorIsEqualByPosition(actual, expected));
      
      // The following test is not based on any published examples
      random.Reset();
      random.DoubleNumbers = new double[] { 0.35, 0.62, 0.18, 0.42, 0.83, 0.76, 0.39, 0.51, 0.36 };
      parent1 = new BinaryVector(new bool[] { false, true, true, false, false }); // this parent is longer
      parent2 = new BinaryVector(new bool[] { false, true, true, false });
      exceptionFired = false;
      try {
        actual = UniformCrossover.Apply(random, parent1, parent2);
      } catch (System.ArgumentException) {
        exceptionFired = true;
      }
      Assert.IsTrue(exceptionFired);
    }

    /// <summary>
    ///A test for SinglePointCrossover Constructor
    ///</summary>
    [TestMethod()]
    public void SinglePointCrossoverConstructorTest() {
      NPointCrossover target = new NPointCrossover();
    }
  }
}