#region License Information
/* HeuristicLab
* Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using HeuristicLab.Core;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Tests;
using Microsoft.VisualStudio.TestTools.UnitTesting;
namespace HeuristicLab.Encodings.BinaryVectorEncoding_33.Tests {
///
///This is a test class for SinglePointCrossoverTest and is intended
///to contain all SinglePointCrossoverTest Unit Tests
///
[TestClass()]
public class UniformCrossoverTest {
private TestContext testContextInstance;
///
///Gets or sets the test context which provides
///information about and functionality for the current test run.
///
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
///
///A test for Cross
///
[TestMethod()]
[DeploymentItem("HeuristicLab.Encodings.BinaryVectorEncoding-3.3.dll")]
public void SinglePointCrossoverCrossTest() {
UniformCrossover_Accessor target = new UniformCrossover_Accessor(new PrivateObject(typeof(UniformCrossover)));
ItemArray 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(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(new BinaryVector[] { new BinaryVector(4) });
exceptionFired = false;
try {
BinaryVector actual;
actual = target.Cross(random, parents);
}
catch (System.ArgumentException) {
exceptionFired = true;
}
Assert.IsTrue(exceptionFired);
}
///
///A test for Apply
///
[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);
}
///
///A test for SinglePointCrossover Constructor
///
[TestMethod()]
public void SinglePointCrossoverConstructorTest() {
NPointCrossover target = new NPointCrossover();
}
}
}