#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 System.IO;
using HeuristicLab.Algorithms.GeneticAlgorithm;
using HeuristicLab.Encodings.LinearLinkageEncoding;
using HeuristicLab.Persistence.Default.Xml;
using HeuristicLab.Problems.Programmable;
using HeuristicLab.Selection;
using Microsoft.VisualStudio.TestTools.UnitTesting;
namespace HeuristicLab.Tests {
[TestClass]
public class GAGroupingProblemSampleTest {
private const string SampleFileName = "GA_Grouping";
#region Code
private const string ProblemCode = @"
using System;
using System.Linq;
using System.Collections.Generic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.LinearLinkageEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Problems.Programmable;
namespace HeuristicLab.Problems.Programmable {
public class CompiledSingleObjectiveProblemDefinition : CompiledProblemDefinition, ISingleObjectiveProblemDefinition {
private const int ProblemSize = 100;
public bool Maximization { get { return false; } }
private bool[,] allowedTogether;
public override void Initialize() {
var encoding = new LinearLinkageEncoding(""lle"", length: ProblemSize);
allowedTogether = new bool[encoding.Length, encoding.Length];
var random = new System.Random(13);
for (var i = 0; i < encoding.Length - 1; i++)
for (var j = i + 1; j < encoding.Length; j++)
allowedTogether[i, j] = allowedTogether[j, i] = random.Next(2) == 0;
Encoding = encoding;
}
public double Evaluate(Individual individual, IRandom random) {
var penalty = 0;
var groups = individual.LinearLinkage(""lle"").GetGroups().ToList();
for (var i = 0; i < groups.Count; i++) {
for (var j = 0; j < groups[i].Count; j++)
for (var k = j + 1; k < groups[i].Count; k++)
if (!allowedTogether[groups[i][j], groups[i][k]]) penalty++;
}
if (penalty > 0) return penalty + ProblemSize;
else return groups.Count;
}
public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) { }
public IEnumerable GetNeighbors(Individual individual, IRandom random) {
foreach (var move in ExhaustiveSwap2MoveGenerator.Generate(individual.LinearLinkage(""lle""))) {
var neighbor = individual.Copy();
var lle = neighbor.LinearLinkage(""lle"");
Swap2MoveMaker.Apply(lle, move);
yield return neighbor;
}
}
}
}
";
#endregion
[TestMethod]
[TestCategory("Samples.Create")]
[TestProperty("Time", "medium")]
public void CreateGaGroupingProblemSampleTest() {
var ga = CreateGaGroupingProblemSample();
string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
XmlGenerator.Serialize(ga, path);
}
[TestMethod]
[TestCategory("Samples.Execute")]
[TestProperty("Time", "long")]
public void RunGaGroupingProblemSampleTest() {
var ga = CreateGaGroupingProblemSample();
ga.SetSeedRandomly.Value = false;
SamplesUtils.RunAlgorithm(ga);
Assert.AreEqual(26, SamplesUtils.GetDoubleResult(ga, "BestQuality"));
Assert.AreEqual(27.58, SamplesUtils.GetDoubleResult(ga, "CurrentAverageQuality"));
Assert.AreEqual(105, SamplesUtils.GetDoubleResult(ga, "CurrentWorstQuality"));
Assert.AreEqual(99100, SamplesUtils.GetIntResult(ga, "EvaluatedSolutions"));
}
private GeneticAlgorithm CreateGaGroupingProblemSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
#region Problem Configuration
var problem = new SingleObjectiveProgrammableProblem() {
ProblemScript = { Code = ProblemCode }
};
problem.ProblemScript.Compile();
#endregion
#region Algorithm Configuration
ga.Name = "Genetic Algorithm - Grouping Problem";
ga.Description = "A genetic algorithm which solves a grouping problem using the linear linkage encoding.";
ga.Problem = problem;
SamplesUtils.ConfigureGeneticAlgorithmParameters(
ga, 100, 1, 1000, 0.05, 2);
#endregion
return ga;
}
}
}