public void GenerateGenomes()
{
var count = 1000000;
var maxDepthRandomGenome = ModelHelper.GetBpmModel().GetMaxDepthRandomGenome();
var genomes = new List <string>();
for (var i = 0; i < count; i++)
{
var genome = new BpmGenome
{
RootGene =
ProcessHelper.ProcessGenerator.GenerateRandomValidBpmGenome2(
maxDepthRandomGenome, null)
};
genomes.Add(genome.ToString());
}
var distinctCount = genomes.Distinct().Count();
Assert.IsTrue(distinctCount >= count * 0.5);
}
private BpmSolution Evaluate(BpmGenome genome)
{
// start time measurement
var startTime = DateTime.Now;
Debug.WriteLine("Evaluating: " + genome);
// load pain factor
painFactor = ModelHelper.GetBpmModel().GetPainFactor();
#region costevaluation
// calculate all process paths
// - splitted by XOR for dependent probabilities
var splitterXor = new ProcessHelper.PathSplitter(genome);
var pathsForProbabilities = splitterXor.GetPaths();
// remove paths with 0.0 percentage probability
pathsForProbabilities.RemoveAll(x => x.Probability == 0);
// calculate probabilities for each activity
//
// [2] Wahrscheinlichkeiten für jede Activity
var activitieProbabilities = new HashSet <BpmnActivity>();
var painFactorActivityAttributeCovering = false;
var painFactorObjectDependencies = 0;
// Calculate probabilities for every activity
CalculateActivityProbabilities(ref activitieProbabilities, pathsForProbabilities);
// Check resulting probabilities with input data
painFactorActivityAttributeCovering = !CheckActivityAttributeCovering(genome.RootGene);
// Check input/ouput realtionships from activities to objects
//painFactorObjectDependencies = !CheckObjectDependencies();
var valide = ProcessHelper.Validator.ValidateGenome(genome, ref painFactorObjectDependencies);
// Calculate µNPV
var mueP = CalculateMueP(valide, activitieProbabilities);
var mueNpv = CalculateMueNpv(genome, mueP);
// Calculate σ^2NPV
var sigma2P = CalculateSigma2P(mueP, activitieProbabilities, pathsForProbabilities);
var sigma2Npv = CalculateSigma2Npv(sigma2P);
// Calculate preference function
var costFitness = mueNpv - ModelHelper.GetBpmModel().GetAlpha() / 2 * sigma2Npv;
Debug.WriteLine("costFitness: " + costFitness + " = " + mueNpv + " - (" +
ModelHelper.GetBpmModel().GetAlpha() + "/2) * " + sigma2Npv);
#endregion
#region timeevaluation
double timeFitness = 0;
foreach (var bpa in DataHelper.ActivityAttributeHelper.Instance().GetAll())
{
timeFitness += bpa.DecisionProbability * CalculateTime(bpa, genome.RootGene);
}
if (DataHelper.ActivityAttributeHelper.Instance().GetAll().Count <= 0)
{
timeFitness = TreeHelper.CalculateLongestTime(genome.RootGene);
}
#endregion
#region fitness merge
var fitness = costFitness * ModelHelper.GetBpmModel().GetCostWeight();
fitness += -1 * timeFitness * ModelHelper.GetBpmModel().GetTimeWeight();
#endregion
if (painFactorActivityAttributeCovering)
{
fitness -= ModelHelper.GetBpmModel().GetPainFactor();
}
if (painFactorObjectDependencies > 0)
{
fitness -= ModelHelper.GetBpmModel().GetPainFactor() * painFactorObjectDependencies;
}
// only when pretty process enabled
if (pretty)
{
fitness -= ModelHelper.GetBpmModel().GetPainFactor() * ProcessHelper.PrettyPrint.Check(genome);
}
var endTime = DateTime.Now;
var activities = activitieProbabilities.Select(x => x.Name).Distinct().ToList();
var index = GeneticAlgorithm.Instance()?.Population?.CurrentGeneration?.GenerationIndex;
if (!index.HasValue)
{
index = -1;
}
var solution = new BpmSolution(
(endTime - startTime).TotalMilliseconds,
index.Value,
fitness,
genome.ToString(),
valide,
"[" + string.Join(", ", activities.ToArray()) + "]",
mueP,
mueNpv,
sigma2P,
sigma2Npv,
timeFitness,
costFitness
);
return(solution);
}