/**
* Inserts the classifier in the population. Before that, it looks if there is a classifier in the
* population with the same action and condition (in this case, increments its numerosity). After,
* it checks that the number of micro classifiers is less than the maximum population size. If it
* isn't, it deletes one classifier from the population calling the deleteClassifier function. It
* inserts the classifier in the population and in the action set if it's not null.
*
* @param cl is the classifier that has to be inserted in the population.
* @param ASet Population where the classifier will be inserted.
*/
public void insertInPopulation(Classifier cl, Population ASet) {
boolean found = false;
int i = 0;
while (i < macroClSum && !found) {
if (set[i].equals(cl)) {
set[i].increaseNumerosity(cl.getNumerosity());
microClSum += cl.getNumerosity();
if (ASet != null) {
if (ASet.isThereClassifier(set[i]) >= 0) ASet.microClSum += cl.getNumerosity();
}
found = true;
}
i++;
}
if (!found) {
addClassifier(cl);
}
// Here, the classifier has been added to the population
if (microClSum
> Config.popSize) { // If we have inserted to many classifiers, we have to delete one.
deleteClFromPopulation(ASet);
}
} // end insertInPopulation
/**
* Inserts the classifier into the population. Before, it looks if there is a classifier in the
* population that can subsume the new one (in this case, increments its numerosity). After, it
* checks that the number of micro classifiers is less than the maximum population size. If it
* isn't, it deletes one classifier of the population calling the deleteClassifier function. It
* inserts the classifier in the population and in the action set if it's not null.
*
* @param cl is the classifier that has to be inserted in the population.
* @param ASet Population where the classifier will be inserted.
*/
public void insertInPSubsumingCl(Classifier cl, Population ASet) {
int i = 0;
Classifier bestSubsumer = null;
Classifier equalClassifier = null;
// We look for the best subsumer or for an equal classifier.
while (i < macroClSum) {
if (set[i].couldSubsume() && set[i].isMoreGeneral(cl)) {
if (bestSubsumer == null) bestSubsumer = set[i];
else if (set[i].isMoreGeneral(bestSubsumer)) bestSubsumer = set[i];
}
if (set[i].equals(cl)) equalClassifier = set[i];
i++;
}
// If there is a subsumer, its numerosity is increased.
if (bestSubsumer != null) {
bestSubsumer.increaseNumerosity(cl.getNumerosity());
microClSum += cl.getNumerosity();
} else if (equalClassifier != null) {
equalClassifier.increaseNumerosity(cl.getNumerosity());
microClSum += cl.getNumerosity();
} else {
addClassifier(cl);
}
// There's no classifier deletion, independent of if the maximum size
// has been overcomen
} // end insertInPSubsumingCl
/**
* Deletes one classifier from the population. After that, if the population passed as a parameter
* is not null, it looks for the deleted classifier. If it is in the second population, it will
* delete it too.
*
* @param aSet is the population where the deleted classifier has to be searched.
* @return a Classifier that contains the deleted classifier.
*/
public Classifier deleteClFromPopulation(Population aSet) {
// A classifier has been deleted from the population
Classifier clDeleted = deleteClassifier();
if (aSet
!= null) { // Now, this classifier has to be deleted from the action set (if it exists in).
int pos = aSet.isThereClassifier(clDeleted); // It is searched in the action set.
if (pos >= 0) { // It has to be deleted from the action set too.
aSet.microClSum--;
// If the classifier has 0 numerosity, we remove it from the population.
if (clDeleted.getNumerosity() == 0) { // It has to be completely deleted from action set.
aSet.macroClSum--; // Decrements the number of macroclassifiers
aSet.set[pos] = aSet.set[aSet.macroClSum]; // Moves the last classifier to the deleted one
aSet.set[aSet.macroClSum] = null; // Puts the last classifier to null.
}
}
}
return clDeleted;
} // end deleteClFromPopulation
/** This method applies the action set subsumption */
public void doActionSetSubsumption() {
int i, pos = 0;
Classifier cl = null;
for (i = 0; i < macroClSum; i++) {
if (set[i].couldSubsume()) {
if (cl == null
|| set[i].numberOfDontCareSymbols() > cl.numberOfDontCareSymbols()
|| (set[i].numberOfDontCareSymbols() == cl.numberOfDontCareSymbols()
&& Config.rand() < 0.5)) {
cl = set[i];
pos = i;
}
}
}
if (cl != null) {
for (i = 0; i < macroClSum; i++) {
if (cl != set[i] && cl.isMoreGeneral(set[i])) {
cl.increaseNumerosity(set[i].getNumerosity());
// Now, the classifier has to be removed from the actionSet and the population.
// It's deleted from the action set.
Classifier clDeleted = set[i];
deleteClassifier(i);
// And now, it's deleted from the population
Population p = parentRef;
while (p.parentRef != null) {
p = p.parentRef;
}
pos =
p.isThereClassifier(
clDeleted); // The classifier is searched in the initial population.
if (pos >= 0) p.deleteClassifier(pos);
}
}
}
} // end doActionSetSubsumption
/**
* Returns if the classifier of the class subsumes the classifier passed as a parameter.
*
* @param cl is the subsumed classifier.
* @return a boolean indicating if it subsumes
*/
public boolean doesSubsume(Classifier cl) {
int i;
// First, check if the condition is the same
if (action != cl.getAction()) return false;
// Then, check that is more general
if (parameters.couldSubsume()) {
for (i = 0; i < rep.length; i++) {
if (!rep[i].isMoreGeneral(cl.rep[i])) return false;
}
return true;
}
return false;
} // end doesSubsume
/**
* Adds a classifier in the population.
*
* @param cl is the new classifier to be added.
*/
public void addClassifier(Classifier cl) {
try {
set[macroClSum] = cl;
microClSum += cl.getNumerosity();
macroClSum++;
} catch (Exception e) {
System.out.println(
"Exception in the insertion of a new classifier. The macroClSum is : "
+ macroClSum
+ " and the microClsum: "
+ microClSum);
System.out.println(
"And the maximum number of classifiers in the population is: " + Config.popSize);
e.printStackTrace();
}
} // end addClassifier
/**
* Returns if the classifier of the class is equal to the classifier given as a parameter.
*
* @param cl is a classifier.
* @return a boolean indicating if they are equals.
*/
public boolean equals(Classifier cl) {
int i;
try {
// Checking the action
if (action != cl.getAction()) return false;
// Checking the condition
for (i = 0; i < rep.length; i++) {
if (!rep[i].equals(cl.rep[i])) return false;
}
return true;
} catch (Exception e) {
return false;
}
} // end equals
/**
* Applies crossover. It generates two children.
*
* @param parent1 is the first parent.
* @param parent2 is the second parent
* @param child1 is the first child
* @param child2 is the second child.
*/
public void makeCrossover(
Classifier parent1, Classifier parent2, Classifier child1, Classifier child2) {
int i = 0;
// cross1 is a number between [0.. clLength-1]
int cross1 = (int) (Config.rand() * (double) Config.clLength);
// cross2 is a number between [1..clLenght].
int cross2 = (int) (Config.rand() * (double) Config.clLength) + 1;
if (cross1 > cross2) {
int aux = cross2;
cross2 = cross1;
cross1 = aux;
// In the else-if condition is not necessary to check if (cross2<clLength)
// to increment the point, because cross1 [0..length-1]
} else if (cross1 == cross2) cross2++;
// All the intervals (real representation) or genes (ternary representation) that
// are not in the cross point are crossed.
if (!Config.ternaryRep) {
for (i = cross1 + 1; i < cross2 - 1; i++) {
child2.setAllele(i, parent1);
child1.setAllele(i, parent2);
}
// Now we have to cross the border allele
child1.crossAllele(cross1, parent1, parent2);
child1.crossAllele(cross2 - 1, parent2, parent1);
child2.crossAllele(cross1, parent2, parent1);
child2.crossAllele(cross2 - 1, parent1, parent2);
} else {
for (i = cross1; i < cross2 - 1; i++) {
child2.setAllele(i, parent1);
child1.setAllele(i, parent2);
}
}
} // end makeCrossover