Index: trunk/sources/HeuristicLab/3.3/Tests/SamplesTest.cs
===================================================================
--- trunk/sources/HeuristicLab/3.3/Tests/SamplesTest.cs (revision 6505)
+++ trunk/sources/HeuristicLab/3.3/Tests/SamplesTest.cs (revision 6544)
@@ -1,38 +1,55 @@
-using System;
-using System.Text;
-using System.Collections.Generic;
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 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;
using System.Linq;
-using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System.Threading;
+using HeuristicLab.Algorithms.EvolutionStrategy;
using HeuristicLab.Algorithms.GeneticAlgorithm;
-using HeuristicLab.Problems.ArtificialAnt;
-using HeuristicLab.Selection;
-using HeuristicLab.Data;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
-using HeuristicLab.Persistence.Default.Xml;
-using HeuristicLab.Optimization;
-using System.Threading;
-using HeuristicLab.ParallelEngine;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
-using HeuristicLab.Problems.DataAnalysis;
-using HeuristicLab.Problems.DataAnalysis.Symbolic;
-using System.IO;
-using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
-using HeuristicLab.Problems.TravelingSalesman;
-using HeuristicLab.Encodings.PermutationEncoding;
-using HeuristicLab.Problems.VehicleRouting;
-using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
-using HeuristicLab.Problems.VehicleRouting.Encodings;
-using HeuristicLab.Problems.VehicleRouting.Encodings.General;
-using HeuristicLab.Algorithms.EvolutionStrategy;
-using HeuristicLab.Encodings.RealVectorEncoding;
-using HeuristicLab.Problems.TestFunctions;
-using HeuristicLab.Optimization.Operators;
using HeuristicLab.Algorithms.LocalSearch;
-using HeuristicLab.Problems.Knapsack;
-using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Algorithms.ParticleSwarmOptimization;
using HeuristicLab.Algorithms.SimulatedAnnealing;
using HeuristicLab.Algorithms.TabuSearch;
using HeuristicLab.Algorithms.VariableNeighborhoodSearch;
+using HeuristicLab.Data;
+using HeuristicLab.Encodings.BinaryVectorEncoding;
+using HeuristicLab.Encodings.PermutationEncoding;
+using HeuristicLab.Encodings.RealVectorEncoding;
+using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.Optimization;
+using HeuristicLab.Optimization.Operators;
+using HeuristicLab.ParallelEngine;
+using HeuristicLab.Persistence.Default.Xml;
+using HeuristicLab.Problems.ArtificialAnt;
+using HeuristicLab.Problems.DataAnalysis;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Classification;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
+using HeuristicLab.Problems.Knapsack;
+using HeuristicLab.Problems.TestFunctions;
+using HeuristicLab.Problems.TravelingSalesman;
+using HeuristicLab.Problems.VehicleRouting;
+using HeuristicLab.Problems.VehicleRouting.Encodings.General;
+using HeuristicLab.Problems.VehicleRouting.Encodings.Potvin;
+using HeuristicLab.Selection;
+using Microsoft.VisualStudio.TestTools.UnitTesting;
namespace HeuristicLab_33.Tests {
@@ -65,5 +82,5 @@
private GeneticAlgorithm CreateGaTspSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
- #region problem configuration
+ #region Problem Configuration
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
@@ -74,5 +91,5 @@
tspProblem.Description = "130 city problem (Churritz)";
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ga.Name = "Genetic Algorithm - TSP";
ga.Description = "A genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
@@ -90,5 +107,4 @@
return ga;
}
-
#endregion
#region VRP
@@ -112,5 +128,5 @@
private GeneticAlgorithm CreateGaVrpSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
- #region problem configuration
+ #region Problem Configuration
VehicleRoutingProblem vrpProblem = new VehicleRoutingProblem();
@@ -130,5 +146,5 @@
vrpProblem.Vehicles.Value = 25;
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ga.Name = "Genetic Algorithm - VRP";
ga.Description = "A genetic algorithm which solves the \"C101\" vehicle routing problem (imported from Solomon)";
@@ -161,8 +177,6 @@
return ga;
}
-
#endregion
#region ArtificialAnt
-
[TestMethod]
public void CreateGpArtificialAntSampleTest() {
@@ -191,5 +205,5 @@
public GeneticAlgorithm CreateGpArtificialAntSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
- #region problem configuration
+ #region Problem Configuration
ArtificialAntProblem antProblem = new ArtificialAntProblem();
antProblem.BestKnownQuality.Value = 89;
@@ -200,5 +214,5 @@
antProblem.MaxTimeSteps.Value = 600;
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ga.Name = "Genetic Programming - Artificial Ant";
ga.Description = "A standard genetic programming algorithm to solve the artificial ant problem (Santa-Fe trail)";
@@ -222,7 +236,6 @@
return ga;
}
-
- #endregion
- #region symbolic regression
+ #endregion
+ #region Symbolic Regression
[TestMethod]
public void CreateGpSymbolicRegressionSampleTest() {
@@ -250,5 +263,5 @@
private GeneticAlgorithm CreateGpSymbolicRegressionSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
- #region problem configuration
+ #region Problem Configuration
SymbolicRegressionSingleObjectiveProblem symbRegProblem = new SymbolicRegressionSingleObjectiveProblem();
symbRegProblem.Name = "Tower Symbolic Regression Problem";
@@ -323,5 +336,5 @@
symbRegProblem.EvaluatorParameter.Value = new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator();
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ga.Problem = symbRegProblem;
ga.Name = "Genetic Programming - Symbolic Regression";
@@ -345,6 +358,5 @@
}
#endregion
- #region symbolic classification
-
+ #region Symbolic Classification
[TestMethod]
public void CreateGpSymbolicClassificationSampleTest() {
@@ -373,5 +385,5 @@
private GeneticAlgorithm CreateGpSymbolicClassificationSample() {
GeneticAlgorithm ga = new GeneticAlgorithm();
- #region problem configuration
+ #region Problem Configuration
SymbolicClassificationSingleObjectiveProblem symbClassProblem = new SymbolicClassificationSingleObjectiveProblem();
symbClassProblem.Name = "Mammography Classification Problem";
@@ -439,5 +451,5 @@
symbClassProblem.EvaluatorParameter.Value = new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator();
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ga.Problem = symbClassProblem;
ga.Name = "Genetic Programming - Symbolic Classification";
@@ -485,5 +497,5 @@
private EvolutionStrategy CreateEsGriewankSample() {
EvolutionStrategy es = new EvolutionStrategy();
- #region problem configuration
+ #region Problem Configuration
SingleObjectiveTestFunctionProblem problem = new SingleObjectiveTestFunctionProblem();
@@ -498,5 +510,5 @@
problem.Description = "Test function with real valued inputs and a single objective.";
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
es.Name = "Evolution Strategy - Griewank";
es.Description = "An evolution strategy which solves the 10-dimensional Griewank test function";
@@ -516,5 +528,4 @@
return es;
}
-
#endregion
#endregion
@@ -540,5 +551,5 @@
private IslandGeneticAlgorithm CreateIslandGaTspSample() {
IslandGeneticAlgorithm ga = new IslandGeneticAlgorithm();
- #region problem configuration
+ #region Problem Configuration
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
@@ -549,5 +560,5 @@
tspProblem.Description = "130 city problem (Churritz)";
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ga.Name = "Island Genetic Algorithm - TSP";
ga.Description = "An island genetic algorithm which solves the \"ch130\" traveling salesman problem (imported from TSPLIB)";
@@ -566,5 +577,4 @@
return ga;
}
-
#endregion
#endregion
@@ -590,5 +600,5 @@
private LocalSearch CreateLocalSearchKnapsackSample() {
LocalSearch ls = new LocalSearch();
- #region problem configuration
+ #region Problem Configuration
KnapsackProblem problem = new KnapsackProblem();
problem.BestKnownQuality = new DoubleValue(362);
@@ -607,5 +617,5 @@
problem.Description = "Represents a Knapsack problem.";
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ls.Name = "Local Search - Knapsack";
ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
@@ -624,10 +634,8 @@
ls.Seed.Value = 0;
ls.SetSeedRandomly.Value = true;
-
#endregion
ls.Engine = new ParallelEngine();
return ls;
}
-
#endregion
#endregion
@@ -659,5 +667,5 @@
private ParticleSwarmOptimization CreatePsoSchwefelSample() {
ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
- #region problem configuration
+ #region Problem Configuration
var problem = new SingleObjectiveTestFunctionProblem();
problem.BestKnownQuality.Value = 0.0;
@@ -669,5 +677,5 @@
problem.SolutionCreator = new UniformRandomRealVectorCreator();
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
pso.Name = "Particle Swarm Optimization - Schwefel";
pso.Description = "A particle swarm optimization algorithm which solves the 2-dimensional Schwefel test function (based on the description in Pedersen, M.E.H. (2010). PhD thesis. University of Southampton)";
@@ -705,5 +713,4 @@
pso.Seed.Value = 0;
pso.SetSeedRandomly.Value = true;
-
#endregion
pso.Engine = new ParallelEngine();
@@ -730,5 +737,5 @@
private SimulatedAnnealing CreateSimulatedAnnealingRastriginSample() {
SimulatedAnnealing sa = new SimulatedAnnealing();
- #region problem configuration
+ #region Problem Configuration
var problem = new SingleObjectiveTestFunctionProblem();
problem.BestKnownQuality.Value = 0.0;
@@ -740,5 +747,5 @@
problem.SolutionCreator = new UniformRandomRealVectorCreator();
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
sa.Name = "Simulated Annealing - Rastrigin";
sa.Description = "A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function";
@@ -799,5 +806,5 @@
private TabuSearch CreateTabuSearchTspSample() {
TabuSearch ts = new TabuSearch();
- #region problem configuration
+ #region Problem Configuration
var tspProblem = new TravelingSalesmanProblem();
tspProblem.ImportFromTSPLIB("ch130.tsp", "ch130.opt.tour", 6110);
@@ -808,5 +815,5 @@
tspProblem.Description = "130 city problem (Churritz)";
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
ts.Name = "Tabu Search - TSP";
ts.Description = "A tabu search algorithm that solves the \"ch130\" TSP (imported from TSPLIB)";
@@ -853,5 +860,4 @@
return ts;
}
-
#endregion
#endregion
@@ -877,5 +883,5 @@
private VariableNeighborhoodSearch CreateVnsTspSample() {
VariableNeighborhoodSearch vns = new VariableNeighborhoodSearch();
- #region problem configuration
+ #region Problem Configuration
TravelingSalesmanProblem tspProblem = new TravelingSalesmanProblem();
tspProblem.BestKnownSolution = new Permutation(PermutationTypes.Absolute, new int[] {
@@ -893,5 +899,5 @@
tspProblem.Description = "Represents a symmetric Traveling Salesman Problem.";
#endregion
- #region algorithm configuration
+ #region Algorithm Configuration
vns.Name = "Variable Neighborhood Search - TSP";
vns.Description = "A variable neighborhood search algorithm which solves a funny TSP instance";
@@ -938,8 +944,8 @@
return vns;
}
-
- #endregion
- #endregion
- #region helper
+ #endregion
+ #endregion
+
+ #region Helpers
private void ConfigureEvolutionStrategyParameters(EvolutionStrategy es, int popSize, int children, int parentsPerChild, int maxGens, bool plusSelection)
where R : ICrossover