#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.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(); } } }