#region License Information
/* HeuristicLab
* Copyright (C) 2002-2016 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.Tests;
using Microsoft.VisualStudio.TestTools.UnitTesting;
namespace HeuristicLab.Encodings.BinaryVectorEncoding.Tests {
///
///This is a test class for SinglePointCrossoverTest and is intended
///to contain all SinglePointCrossoverTest Unit Tests
///
[TestClass()]
public class UniformCrossoverTest {
///
///A test for Apply
///
[TestMethod]
[TestCategory("Encodings.BinaryVector")]
[TestProperty("Time", "short")]
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);
}
}
}