Population-based incremental learning: Difference between revisions

Revision as of 19:32, 13 June 2009

Revision as of 19:58, 13 June 2009

Line 53:

for (int i = 0; i < ITER_COUNT; i++) {

// Creates N genotypesgenes

final boolean[][] genoTypesgenes = new boolean[N][totalBits];

for (boolean[] genoTypegene : genoTypesgenes) {

for (int k = 0; k < genoTypegene.length; k++) {

if (rand.nextDouble() < probVec[k])

genoTypegene[k] = true;

}

}

Line 65:

final double[] costs = new double[N];

for (int j = 0; j < N; j++) {

costs[j] = costFunc.cost(toRealVec(genoTypesgenes[j], domains));

}

// Find min and max cost genotypesgenes

boolean[] minGenoTypeminGene = null, maxGenoTypemaxGene = null;

double minCost = POSITIVE_INFINITY, maxCost = NEGATIVE_INFINITY;

for (int j = 0; j < N; j++) {

Line 75:

if (minCost > cost) {

minCost = cost;

minGenoTypeminGene = genoTypesgenes[j];

}

if (maxCost < cost) {

maxCost = cost;

maxGenoTypemaxGene = genoTypesgenes[j];

}

}

// Compare with the best cost genotypesgene

if (bestCost > minCost) {

bestCost = minCost;

bestGenoTypebestGene = minGenoTypeminGene;

}

// Update the probability vector with max and min cost genotypesgenes

for (int j = 0; j < totalBits; j++) {

if (minGenoTypeminGene[j] == maxGenoTypemaxGene[j]) {

probVec[j] = probVec[j] * (1d - learnRate) +

(minGenoTypeminGene[j] ? 1d : 0d) * learnRate;

} else {

final double learnRate2 = learnRate + negLearnRate;

probVec[j] = probVec[j] * (1d - learnRate2) +

(minGenoTypeminGene[j] ? 1d : 0d) * learnRate2;

}

}