public float computeFitness(Individual individual) {
for (int i = 0; i < Problem.n; ) { // Empty increment
char c = individual.getAllele(i++);
for (int j = 1; j < 6; j++) if (individual.getAllele(i++) != c) return (float) 0;
}
return (float) 1;
}
// Compute scores for new leaf0 and leaf1, and add them to scoreList.
private void computeNewLeafScores(SelectedSet selectedSet, int i, int j) {
int NS = selectedSet.getN();
for (int a = 0; a < NS; a++) {
Individual individual = selectedSet.getIndividual(a);
IGraph iterator = decisionGraphs[i].getGraph();
while (!(iterator
instanceof Leaf)) { // Iterator is a variable because we are still traversing the DG.
int x = ((Variable) iterator).getVariable();
char alleleX = individual.getAllele(x);
if (alleleX == '0') {
iterator = ((Variable) iterator).getZero();
} else {
iterator = ((Variable) iterator).getOne();
}
}
int itrPosition = decisionGraphs[i].getLeafs().indexOf((Leaf) iterator);
if (itrPosition == j || itrPosition == j + 1) { // We've reached one of the new leafs.
int mZero = ((Leaf) iterator).getMZero();
int mOne = ((Leaf) iterator).getMOne();
if (mZero > 0 && mOne > 0) { // It's still "interesting" to split.
char alleleI = individual.getAllele(i); // Value of Xi in individual a.
for (int split : splitList[i]) {
char alleleS = individual.getAllele(split); // Value of Xsplit in individual a.
if (alleleI == '0') {
if (alleleS == '0') {
((Leaf) iterator).addPossibleSplitFrequency(0, split); // m00[split]++;
} else {
((Leaf) iterator).addPossibleSplitFrequency(1, split); // m01[split]++;
}
} else if (alleleS == '0') {
((Leaf) iterator).addPossibleSplitFrequency(2, split); // m10[split]++;
} else {
((Leaf) iterator).addPossibleSplitFrequency(3, split); // m11[split]++;
}
}
}
}
}
for (int a = 0; a <= 1; a++) {
Leaf newLeaf =
decisionGraphs[i].getLeaf(j + a); // The two new leafs are at positions 'j' and 'j+1'.
int mZero = newLeaf.getMZero();
int mOne = newLeaf.getMOne();
if (mZero > 0 && mOne > 0) {
for (int s : splitList[i]) {
int m00 = newLeaf.getPossibleSplitFrequency(0, s);
int m01 = newLeaf.getPossibleSplitFrequency(1, s);
int m10 = newLeaf.getPossibleSplitFrequency(2, s);
int m11 = newLeaf.getPossibleSplitFrequency(3, s);
double scoreGain = bayesianMetric.computeScoreGain(mZero, mOne, m00, m01, m10, m11);
newLeaf.setScoreGain(s, scoreGain);
newLeaf.updateBestSplit(
s,
scoreGain); // Responsible for updating the value of the best split score gain in this
// leaf.
}
}
} // END: for(int a = 0 ...)
} // END: computeNewLeafScores(...)
public float computeFitness(Individual individual) {
int six;
float fit = 0;
for (int i = 0; i < Problem.n; ) { // Empty increment
six = Integer.parseInt(String.valueOf(individual.getAllele(i++)));
six += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
six += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
six += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
six += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
six += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
six = Math.abs(six - 3);
switch (six) {
case 0:
fit += 0.9;
break;
case 1:
fit += 0.8;
break;
case 3:
fit += 1;
break;
default:
break;
}
}
return fit;
}
public float computeFitness(Individual individual) {
int k;
float fit = 0;
for (int i = 0; i < Problem.n; ) { // Empty increment
k = Integer.parseInt(String.valueOf(individual.getAllele(i++)));
for (int j = 1; j < kay; j++)
k += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
if (k < kay) fit += kay - 1 - k;
else fit += kay;
}
return fit;
}
public float computeFitness(Individual individual) {
float fit = 0;
for (int i = 0; i < Problem.n; ) { // Empty increment
char a = individual.getAllele(i++);
char b = individual.getAllele(i++);
if (a == '1' && b == '1') fit += 0.9;
if (a == '0' && b == '0') fit += 1;
}
return fit;
}
public float computeFitness(Individual individual) {
int three;
float fit = 0;
for (int i = 0; i < Problem.n; ) { // Empty increment
three = Integer.parseInt(String.valueOf(individual.getAllele(i++)));
three += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
three += Integer.parseInt(String.valueOf(individual.getAllele(i++)));
switch (three) {
case 0:
fit += 1;
break;
case 2:
fit += 0.8;
break;
case 3:
fit += 0.9;
break;
default:
break;
}
}
return fit;
}
public float computeFitness(Individual individual) {
int fit = 0;
for (int i = 0; i < Problem.n; i++) if (individual.getAllele(i) == '0') fit++;
return (float) fit;
}