public override bool Execute()
{
string aim = "Hello friends";
int dim = aim.Length;
Func <string, double> fn1 = (s => s.Select((b, i) => ((int)b - aim[i]) * 2 * ((int)b - aim[i])).Sum());
Func <string, double> fn = (s => fn1(s));
SimulatedAnnealing <string> sa = new SimulatedAnnealing <string>()
{
NeighborhoodProvider = Neighbors,
EnergyProvider = fn,
CurrentElement = "^ùsprôqù;rqô;ùvere!wlpc\"!".Substring(0, dim)
};
for (int i = 0; i < 100; ++i)
{
sa.Run(100);
sa.CurrentElement = sa.BestFound;
}
if (IsVerbose)
{
Console.WriteLine(" ::!:: " + sa.BestFound);
}
if (sa.BestFound != aim)
{
return(false);
}
return(true);
}
static List <Simulation> RunSimulatedAnnealing(Simulation s, int duration)
{
SimulatedAnnealing SA = HeuristicFactory.CreateSA(s, duration, SimulatedAnnealingBase.DebugLevel.All);
SA.Run();
return(SA.BestSolutions);
}
private static void Main(string[] args)
{
var voc = Vocabulary.GetVocabularyFromFile(@"Vocabulary.json");
var grammar = new Grammar(voc);
ProgramParams programParams;
using (var file = File.OpenText(@"ProgramParameters.json"))
{
var serializer = new JsonSerializer();
programParams = (ProgramParams)serializer.Deserialize(file, typeof(ProgramParams));
}
if (programParams.DataWithMovement)
{
CreateMovementGrammar(grammar);
}
else
{
CreateSimpleGrammar(grammar);
}
grammar.GenerateDerivedRulesFromSchema();
var p = new Parser(grammar);
var data = p.GenerateSentences(programParams.NumberOfDataSentences);
using (var sw = File.AppendText("SessionReport.txt"))
{
sw.WriteLine("-------------------");
sw.WriteLine("Session {0} ", DateTime.Now.ToString("MM/dd/yyyy h:mm tt"));
sw.WriteLine("sentences: {0}, runs: {1}, movement: {2}", programParams.NumberOfDataSentences,
programParams.NumberOfRuns, programParams.DataWithMovement);
}
var stopWatch = StartWatch();
var learner = new Learner(voc, grammar.NonTerminalsTypeDictionary, grammar.POSTypes, data, grammar);
learner.originalGrammar.GenerateDerivedRulesFromSchema();
var targetGrammarEnergy = learner.Energy(learner.originalGrammar);
learner.originalGrammar.GenerateInitialRulesFromDerivedRules();
var s = string.Format("Target Hypothesis:\r\n{0} with energy: {1}\r\n", learner.originalGrammar,
targetGrammarEnergy);
Console.WriteLine(s);
using (var sw = File.AppendText("SessionReport.txt"))
{
sw.WriteLine(s);
}
for (var i = 0; i < programParams.NumberOfRuns; i++)
{
var sa = new SimulatedAnnealing(learner);
sa.Run();
}
StopWatch(stopWatch);
}
/// <summary>
/// Tests a simulated annealing.
/// </summary>
/// <typeparam name="TGene"></typeparam>
/// <param name="testNumber">The number of the test.</param>
/// <param name="testDescription">The description of the test.</param>
/// <param name="simulatedAnnealing">The simulated annealing to test.</param>
/// <param name="objectiveFunction">The objective funtion to test.</param>
///
/// <param name="maxIterationCount">The maximum number of iterations (the computational budget).</param>
/// <param name="acceptableEnergy">The acceptable energy.</param>
///
/// <param name="initialTemperature">The initial temperature.</param>
/// <param name="finalTemperature">The final temperature.</param>
/// <apra
private static void Test <T>(int testNumber, string testDescription, SimulatedAnnealing <T> simulatedAnnealing, ObjectiveFunction <T> objectiveFunction,
int maxIterationCount, double acceptableEnergy,
double initialTemperature, double finalTemperature)
{
// Print the number of the test and its description.
Console.WriteLine("Test " + testNumber + " : " + testDescription);
// Run the simulated annealing.
DateTime startTime = DateTime.Now;
int usedIterationCount;
double achievedEnergy;
T[] solution = simulatedAnnealing.Run(objectiveFunction,
maxIterationCount, out usedIterationCount, acceptableEnergy, out achievedEnergy,
initialTemperature, finalTemperature
);
DateTime endTime = DateTime.Now;
// Build the solution string.
StringBuilder solutionSB = new StringBuilder();
solutionSB.Append("[");
foreach (T component in solution)
{
solutionSB.Append(component + ", ");
}
if (solution.Length != 0)
{
solutionSB.Remove(solutionSB.Length - 2, 2);
}
solutionSB.Append("]");
// Print the results.
Console.WriteLine("Test " + testNumber + " : Duration: " + (endTime - startTime));
Console.WriteLine("Test " + testNumber + " : Number of iterations taken : " + usedIterationCount);
Console.WriteLine("Test " + testNumber + " : Best solution : " + solutionSB.ToString());
Console.WriteLine("Test " + testNumber + " : Best solution's evaluation : " + achievedEnergy);
}
private static void Main()
{
Parameters.Verbosity = VerbosityLevels.Normal;
// this next line is to set the Debug statements from OOOT to the Console.
Trace.Listeners.Add(new TextWriterTraceListener(Console.Out));
/* In this example, we first present how the details of an optimzation
* problem can be saved to an XML-file so that it can be read in
* and solved as opposed to defining all the details in an imperative
* (code line by code line) way. In the first function, the xml file
* name "test1.xml" is created. */
makeAndSaveProblemDefinition();
/* now we create a series of different optimization methods and test
* them on the problem. The problem is now opened from the file and
* the details are stored in an object of class "Problem Definition".*/
var stream = new FileStream(filename, FileMode.Open);
double[] xStar;
ProblemDefinition probTest1 = ProblemDefinition.OpenprobFromXml(stream);
abstractOptMethod opty;
/******************Exhaustive Search ***********************/
//SearchIO.output("******************Exhaustive Search ***********************");
//Console.ReadKey();
//opty = new ExhaustiveSearch(probTest1.SpaceDescriptor, optimize.minimize);
//opty.Add(probTest1);
///* No convergence criteria is needed as the process concludes when all
// * states have been visited but for this problem that is 4 trillion states.*/
//opty.ConvergenceMethods.Clear();
///* if you DID KNOW the best, you can include a criteria like...*/
//opty.ConvergenceMethods.Add(new ToKnownBestXConvergence(new[] { 3.0, 3.0 }, 0.0000001));
//var timer = Stopwatch.StartNew();
//var fStar = opty.Run(out xStar);
///* you probably will never see this process complete. Even with the added
// * convergence criteria (which is not factored into the estimated time of
// * completion), you are probably looking at 1 to 2 years. */
//printResults(opty, xStar, fStar, timer);
/***********Gradient Based Optimization with Steepest Descent****************/
//SearchIO.output("***********Gradient Based Optimization with Steepest Descent****************");
//Console.ReadKey();
//opty = new GradientBasedOptimization();
//opty.Add(probTest1);
//abstractSearchDirection searchDirMethod = new SteepestDescent();
//opty.Add(searchDirMethod);
////abstractLineSearch lineSearchMethod = new ArithmeticMean(0.0001, 1, 100);
////abstractLineSearch lineSearchMethod = new DSCPowell(0.0001, 1, 100);
//abstractLineSearch lineSearchMethod = new GoldenSection(0.0001, 1);
//opty.Add(lineSearchMethod);
//opty.Add(new squaredExteriorPenalty(opty, 10));
///* since this is not a population-based optimization method, we need to remove the MaxSpan criteria. */
//opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
///***********Gradient Based Optimization with Fletcher-Reeves****************/
//SearchIO.output("***********Gradient Based Optimization with Fletcher-Reeves****************");
//Console.ReadKey();
///* we don't need to reset (invoke the constructor) for GradientBasedOptimization since we are only
// * change the seaach direction method. */
//searchDirMethod = new FletcherReevesDirection();
///* you could also try the remaining 3 search direction methods. */
////searchDirMethod = new CyclicCoordinates();
////searchDirMethod = new BFGSDirection();
////searchDirMethod = new PowellMethod(0.001, 6);
//opty.Add(searchDirMethod);
//timer = Stopwatch.StartNew();
//opty.ResetFunctionEvaluationDatabase();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
///******************Generalized Reduced Gradient***********************/
//SearchIO.output("******************Generalized Reduced Gradient***********************");
//Console.ReadKey();
//opty = new GeneralizedReducedGradientActiveSet();
//opty.Add(probTest1);
//opty.Add(new squaredExteriorPenalty(opty, 10));
//opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
/* GRG is the ONLY one here that handles constraints explicity. It find the
* optimal very quickly and accurately. However, many of the other show a
* better value of f*, this is because they are using an imperfect penalty
* function (new squaredExteriorPenalty(opty, 10)). While it seems that GRG
* includes it as well, it is only used in the the line search method. */
/******************Random Hill Climbing ***********************/
probTest1.SpaceDescriptor = new DesignSpaceDescription(new[] { new VariableDescriptor(-5000, 5000, 0.1),
new VariableDescriptor(-5000, 5000, 0.1) });
SearchIO.output("******************Random Hill Climbing ***********************");
Console.ReadKey();
opty = new HillClimbing();
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 8));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor));
opty.Add(new KeepSingleBest(optimize.minimize));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
var timer = Stopwatch.StartNew();
var fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Exhaustive Hill Climbing ***********************/
SearchIO.output("******************Exhaustive Hill Climbing ***********************");
Console.ReadKey();
/* Everything else about the Random Hill Climbing stays the same. */
opty.Add(new ExhaustiveNeighborGenerator(probTest1.SpaceDescriptor));
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Simulated Annealing***********************/
SearchIO.output("******************Simulated Annealing***********************");
Console.ReadKey();
opty = new SimulatedAnnealing(optimize.minimize);
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 10));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor, 100));
opty.Add(new SACoolingSangiovanniVincentelli(100));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
}
public void NelderOtherHeuristics()
{
int thinningPeriod = 4;
int treeCount = 75;
#if DEBUG
treeCount = 25;
#endif
PlotsWithHeight nelder = PublicApi.GetNelder();
OrganonConfiguration configuration = OrganonTest.CreateOrganonConfiguration(new OrganonVariantNwo());
configuration.Treatments.Harvests.Add(new ThinByIndividualTreeSelection(thinningPeriod));
OrganonStand stand = nelder.ToOrganonStand(configuration, 20, 130.0F, treeCount);
stand.PlantingDensityInTreesPerHectare = TestConstant.NelderReplantingDensityInTreesPerHectare;
Objective landExpectationValue = new Objective()
{
IsLandExpectationValue = true,
PlanningPeriods = 9
};
Objective volume = new Objective()
{
PlanningPeriods = landExpectationValue.PlanningPeriods
};
HeuristicParameters defaultParameters = new HeuristicParameters()
{
UseScaledVolume = false
};
GeneticParameters geneticParameters = new GeneticParameters(treeCount)
{
PopulationSize = 7,
MaximumGenerations = 5,
UseScaledVolume = defaultParameters.UseScaledVolume
};
GeneticAlgorithm genetic = new GeneticAlgorithm(stand, configuration, landExpectationValue, geneticParameters);
TimeSpan geneticRuntime = genetic.Run();
GreatDeluge deluge = new GreatDeluge(stand, configuration, volume, defaultParameters)
{
RainRate = 5,
LowerWaterAfter = 9,
StopAfter = 10
};
deluge.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan delugeRuntime = deluge.Run();
RecordTravel recordTravel = new RecordTravel(stand, configuration, landExpectationValue, defaultParameters)
{
StopAfter = 10
};
recordTravel.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan recordRuntime = recordTravel.Run();
SimulatedAnnealing annealer = new SimulatedAnnealing(stand, configuration, volume, defaultParameters)
{
Iterations = 100
};
annealer.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan annealerRuntime = annealer.Run();
TabuParameters tabuParameters = new TabuParameters()
{
UseScaledVolume = defaultParameters.UseScaledVolume
};
TabuSearch tabu = new TabuSearch(stand, configuration, landExpectationValue, tabuParameters)
{
Iterations = 7,
//Jump = 2,
MaximumTenure = 5
};
tabu.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan tabuRuntime = tabu.Run();
ThresholdAccepting thresholdAcceptor = new ThresholdAccepting(stand, configuration, volume, defaultParameters);
thresholdAcceptor.IterationsPerThreshold.Clear();
thresholdAcceptor.Thresholds.Clear();
thresholdAcceptor.IterationsPerThreshold.Add(10);
thresholdAcceptor.Thresholds.Add(1.0F);
thresholdAcceptor.RandomizeTreeSelection(TestConstant.Default.SelectionPercentage);
TimeSpan acceptorRuntime = thresholdAcceptor.Run();
RandomGuessing random = new RandomGuessing(stand, configuration, volume, defaultParameters)
{
Iterations = 4
};
TimeSpan randomRuntime = random.Run();
configuration.Treatments.Harvests.Clear();
configuration.Treatments.Harvests.Add(new ThinByPrescription(thinningPeriod));
PrescriptionParameters prescriptionParameters = new PrescriptionParameters()
{
Maximum = 60.0F,
Minimum = 50.0F,
Step = 10.0F,
UseScaledVolume = defaultParameters.UseScaledVolume
};
PrescriptionEnumeration enumerator = new PrescriptionEnumeration(stand, configuration, landExpectationValue, prescriptionParameters);
TimeSpan enumerationRuntime = enumerator.Run();
// heuristics assigned to volume optimization
this.Verify(deluge);
this.Verify(annealer);
this.Verify(thresholdAcceptor);
this.Verify(random);
// heuristics assigned to net present value optimization
this.Verify(genetic);
this.Verify(enumerator);
this.Verify(recordTravel);
this.Verify(tabu);
HeuristicSolutionDistribution distribution = new HeuristicSolutionDistribution(1, thinningPeriod, treeCount);
distribution.AddRun(annealer, annealerRuntime, defaultParameters);
distribution.AddRun(deluge, delugeRuntime, defaultParameters);
distribution.AddRun(thresholdAcceptor, acceptorRuntime, defaultParameters);
distribution.AddRun(genetic, geneticRuntime, defaultParameters);
distribution.AddRun(enumerator, enumerationRuntime, defaultParameters);
distribution.AddRun(recordTravel, recordRuntime, defaultParameters);
distribution.AddRun(tabu, tabuRuntime, defaultParameters);
distribution.AddRun(random, randomRuntime, defaultParameters);
distribution.OnRunsComplete();
}