private void calculatePartialFitness() {
ProcessInstance pi = null;
int numSimilarPIs = 0;
int numMissingTokens = 0;
int numExtraTokensLeftBehind = 0;
Iterator logReaderInstanceIterator = logReader.instanceIterator();
while (logReaderInstanceIterator.hasNext()) {
pi = (ProcessInstance) logReaderInstanceIterator.next();
numSimilarPIs = MethodsForWorkflowLogDataStructures.getNumberSimilarProcessInstances(pi);
for (int i = 0; i < population.length; i++) {
if (mapping.getMap(i) < 0) { // we need to compute the partial
// fitness
parser[i].parse(pi);
// partial assignment to variables
numMissingTokens = parser[i].getNumMissingTokens();
if (numMissingTokens > 0) {
this.numPIsWithMissingTokens[i] += numSimilarPIs;
this.numMissingTokens[i] += (numMissingTokens * numSimilarPIs);
}
numExtraTokensLeftBehind = parser[i].getNumExtraTokensLeftBehind();
if (numExtraTokensLeftBehind > 0) {
this.numPIsWithExtraTokensLeftBehind[i] += numSimilarPIs;
this.numExtraTokensLeftBehind[i] += (numExtraTokensLeftBehind * numSimilarPIs);
}
numParsedWMEs[i] += (parser[i].getNumParsedElements() * numSimilarPIs);
numEnabledWMEs[i] += (parser[i].getNumTotalEnabledElements() * numSimilarPIs);
}
}
}
}
private void createParser() {
// creating a parser for every individual
parser = new ExtraBehaviorParser[population.length];
for (int i = 0; i < parser.length; i++) {
if (mapping.getMap(i) < 0) {
parser[i] = new ExtraBehaviorParser(population[i], generator);
}
}
}
private HeuristicsNet[] assignFitness() {
double fitness = 0;
double f1 = 0;
double f2 = 0;
double f3 = 0;
double numATEsAtLog = 0;
double numPIsAtLog = 0;
double missingTokensDenominator = 0.001;
double unusedTokensDenominator = 0.001;
double maxEnabledWMEs = Double.MIN_VALUE;
int indexIdenticalIndividual = 0;
numATEsAtLog = logReader.getLogSummary().getNumberOfAuditTrailEntries();
numPIsAtLog = logReader.getLogSummary().getNumberOfProcessInstances();
maxEnabledWMEs = getMaxValue(numEnabledWMEs);
for (int i = 0; i < population.length; i++) {
indexIdenticalIndividual = mapping.getMap(i);
if (indexIdenticalIndividual < 0) {
missingTokensDenominator = numPIsAtLog - numPIsWithMissingTokens[i] + 1;
unusedTokensDenominator = numPIsAtLog - numPIsWithExtraTokensLeftBehind[i] + 1;
f1 =
(numParsedWMEs[i]
- ((numMissingTokens[i] / missingTokensDenominator)
+ (numExtraTokensLeftBehind[i] / unusedTokensDenominator)))
/ numATEsAtLog;
f2 = numEnabledWMEs[i];
f2 /= maxEnabledWMEs; // normalizing...
if (maximumNumberIntersectingElements > 0) {
f3 =
(numIntersectingDuplicatesPerInputElement[i]
+ numIntersectingDuplicatesPerOutputElement[i])
/ maximumNumberIntersectingElements;
} else {
f3 = 0.0;
}
// f3 = (numIntersectingDuplicatesPerInputElement[i] +
// numIntersectingDuplicatesPerOutputElement[i]) / (2 *
// (Math.pow(population[i].size(),
// 2) - population[i].size()));
fitness = f1 - kappa * f2 - gamma * f3;
// final double k = 1.0/3.0;
// fitness = k*f1 + (k - (k*f2)) + (k - (k*f3));
population[i].setFitness(fitness);
} else {
population[i].setFitness(population[indexIdenticalIndividual].getFitness());
population[i].setDuplicatesActualFiring(
population[indexIdenticalIndividual].getDuplicatesActualFiring());
population[i].setArcUsage(population[indexIdenticalIndividual].getArcUsage());
}
}
return population;
}