/// <summary>
/// Runs the optimisation.
/// </summary>
/// <param name="storeAll"><see langword="true"/> to store all individuals evaluated (memory required).</param>
/// <param name="reportingFrequency">The number of evaluations between reporting progress.</param>
/// <param name="timeOutEvaluations">The maximum number of evaluations before terminating the optimisation.</param>
/// <param name="timeOutDuration">The maximum time allowed before terminating the optimisation.</param>
/// <param name="newIndividualsPerGeneration">The number of new <see cref="Individual"/>s to generate each time new individuals are generated from the <see cref="Population"/>.</param>
public override void Run(
bool storeAll = true,
int reportingFrequency = 100,
int timeOutEvaluations = 0,
TimeSpan?timeOutDuration = null,
int newIndividualsPerGeneration = 1)
{
// Initialise
StartTime = DateTime.Now;
var optimiser = builder.CreateOptimiser();
var model = builder.CreateModel();
cancelDemanded = false;
//Setup
if (newIndividualsPerGeneration <= 0)
{
throw new ArgumentOutOfRangeException(nameof(newIndividualsPerGeneration),
"At least one new individual must be created each generation.");
}
var nextInds = optimiser.GetNextToEvaluate(newIndividualsPerGeneration);
if (timeOutEvaluations == 0)
{
var numDims = nextInds[0].DecisionVector.Count;
timeOutEvaluations = Math.Min(numDims * 20000, 2000000);
}
var timeOutDurationNotNull = TimeSpan.MaxValue;
if (timeOutDuration != null)
{
timeOutDurationNotNull = timeOutDuration.Value;
}
timeOutManager = new TimeOutManager(timeOutEvaluations, timeOutDurationNotNull);
AllEvaluated = new List <Individual>();
FinalPopulation = null;
BestFound = null;
//Go!
while (nextInds.Count > 0 && nextInds[0].DecisionVector.Count > 0)
{
foreach (var nextInd in nextInds)
{
nextInd.SetProperty(
OptimiserPropertyNames.CreationIndex,
timeOutManager.EvaluationsRun);
// Evaluate
model.PrepareForEvaluation(nextInd);
evaluator.Evaluate(nextInd);
}
// Reinsert
optimiser.ReInsert(nextInds);
foreach (var nextInd in nextInds)
{
nextInd.SetProperty(
OptimiserPropertyNames.ReinsertionIndex,
timeOutManager.EvaluationsRun);
timeOutManager.IncrementEvaluationsRun();
}
// Store
if (storeAll)
{
AllEvaluated.AddRange(nextInds);
}
// Update best
var bestInd = optimiser.Population.Best();
if (BestFound == null || (bestInd != null && bestInd.Fitness < BestFound.Fitness))
{
BestFound = bestInd;
}
// Create individuals for next loop
nextInds = optimiser.GetNextToEvaluate(newIndividualsPerGeneration);
// Check for completion
if (timeOutManager.HasPerformedTooManyEvaluations() ||
timeOutManager.HasRunOutOfTime() ||
cancelDemanded)
{
//Bored...
break;
}
if (optimiser.Population.IsTargetSizeReached)
{
if (convergenceCheckers(optimiser.Population))
{
break;
}
}
if (timeOutManager.EvaluationsRun % reportingFrequency == 0)
{
reporters(optimiser.Population);
}
}
reporters(optimiser.Population);
//Finish off
FinalPopulation = optimiser.Population;
}
/// <summary>
/// Runs the optimisation.
/// </summary>
/// <param name="storeAll"><see langword="true"/> to store all individuals evaluated (memory required).</param>
/// <param name="reportingFrequency">The number of evaluations between reporting progress.</param>
/// <param name="timeOutEvaluations">The maximum number of evaluations before terminating the optimisation.</param>
/// <param name="timeOutDuration">The maximum time allowed before terminating the optimisation.</param>
/// <param name="newIndividualsPerGeneration">The number of new <see cref="Individual"/>s to generate each time new individuals are generated from the <see cref="Population"/>.</param>
public override void Run(
bool storeAll = true,
int reportingFrequency = 100,
int timeOutEvaluations = 0,
TimeSpan?timeOutDuration = null,
int newIndividualsPerGeneration = 1)
{
StartTime = DateTime.Now;
// Calculate time outs automatically if not provided
if (timeOutEvaluations == 0)
{
var numDims =
builder.CreateModel().GetNewDecisionVector().Count;
timeOutEvaluations = Math.Min(numDims * 20000, 2000000);
}
var timeOutDurationNotNull = TimeSpan.MaxValue;
if (timeOutDuration != null)
{
timeOutDurationNotNull = timeOutDuration.Value;
}
var timeOutManager = new TimeOutManager(timeOutEvaluations, timeOutDurationNotNull);
if (newIndividualsPerGeneration <= 0)
{
throw new ArgumentOutOfRangeException(nameof(newIndividualsPerGeneration),
"At least one new individual must be created each generation.");
}
setUpAgents(timeOutManager, reportingFrequency, newIndividualsPerGeneration);
if (reinsertionAgent == null || evaluationAgent == null)
{
throw new ApplicationException("Failed to initialise TPL buffers.");
}
reinsertionAgent.SaveAll = storeAll;
// Get started
var pumpPrimingInds = reinsertionAgent.CreateNewIndividuals(
Math.Max(NumberOfIndividualsToStart, newIndividualsPerGeneration));
foreach (var ind in pumpPrimingInds)
{
reinsertionAgent.NewIndividuals.Post(ind);
}
// Wait for completion
try
{
Task.WaitAll(
reinsertionAgent.IndividualsForReinsertion.Completion,
reinsertionAgent.NewIndividuals.Completion,
evaluationAgent.EvaluatedIndividuals.Completion,
evaluationAgent.IndividualsForEvaluation.Completion);
}
catch (AggregateException e) when(e.InnerExceptions.All(ie => ie is TaskCanceledException))
{
// There is no way to wait for cancellation,
// so we have to wait for completion and then
// ignore the cancellation errors
}
FinalPopulation = reinsertionAgent.GetCurrentPopulation();
AllEvaluated = reinsertionAgent.AllEvaluated;
if (FinalPopulation.Count <= 0)
{
return;
}
// Not really the best found unless the optimiser is elitist
BestFound = FinalPopulation.Best();
}
