source: branches/GeneralizedQAP/UnitTests/CordeauCrossoverTest.cs @ 15490

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Linq;
using System.Threading;
using HeuristicLab.Data;
using HeuristicLab.Problems.GeneralizedQuadraticAssignment;
using HeuristicLab.Problems.Instances.CordeauGQAP;
using HeuristicLab.Random;
using Microsoft.VisualStudio.TestTools.UnitTesting;

namespace UnitTests {
  [TestClass]
  public class CordeauCrossoverTest {
    [TestMethod]
    public void CordeauCrossoverFunctionalityTest() {
      var provider = new CordeauGQAPInstanceProvider();
      var instance = provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name == "20-15-75"));
      var gqap = new GeneralizedQuadraticAssignmentProblem();
      gqap.Load(instance);
      var evaluator = gqap.Evaluator;

      for (int i = 0; i < 100; i++) {
        var random = new MersenneTwister((uint)i);
        var parent1 = GreedyRandomizedSolutionCreator.CreateSolution(random, gqap.Demands, gqap.Capacities, gqap.Evaluator, 100, true, CancellationToken.None);
        var parent2 = GreedyRandomizedSolutionCreator.CreateSolution(random, gqap.Demands, gqap.Capacities, gqap.Evaluator, 100, true, CancellationToken.None);

        try {
          CordeauCrossover.Apply(random, gqap.Maximization,
            parent1,
            new DoubleValue(gqap.Evaluator.Evaluate(parent1, gqap.Weights, gqap.Distances, gqap.InstallationCosts, gqap.Demands, gqap.Capacities, gqap.TransportationCosts, gqap.ExpectedRandomQuality)),
            parent2,
            new DoubleValue(gqap.Evaluator.Evaluate(parent2, gqap.Weights, gqap.Distances, gqap.InstallationCosts, gqap.Demands, gqap.Capacities, gqap.TransportationCosts, gqap.ExpectedRandomQuality)),
            gqap.Weights, gqap.Distances, gqap.InstallationCosts, gqap.Demands, gqap.Capacities, gqap.TransportationCosts, gqap.ExpectedRandomQuality, gqap.Evaluator,
            new IntValue(0));
        } catch {
          Assert.Fail("Error during crossover");
        }
      }
    }
  }
}