public void optimize() {
    final int totalBits = getTotalBits(domains);
    final double[] probVec = new double[totalBits];
    Arrays.fill(probVec, 0.5);
    bestCost = POSITIVE_INFINITY;
 
    for (int i = 0; i < ITER_COUNT; i++) {
        // N個の遺伝子を作成する
        final boolean[][] genes = new boolean[N][totalBits];
        for (boolean[] gene : genes) {
            for (int k = 0; k < gene.length; k++) {
                if (rand.nextDouble() < probVec[k])
                    gene[k] = true;
            }
        }

        // コストを計算する
        final double[] costs = new double[N];
        for (int j = 0; j < N; j++) {
            costs[j] = costFunc.cost(toRealVec(genes[j], domains));
        }

        // 最小・最大コストの遺伝子を探す
        boolean[] minGene = null, maxGene = null;
        double minCost = POSITIVE_INFINITY, maxCost = NEGATIVE_INFINITY;
        for (int j = 0; j < N; j++) {
            double cost = costs[j];
            if (minCost > cost) {
                minCost = cost;
                minGene = genes[j];
            }
            if (maxCost < cost) {
                maxCost = cost;
                maxGene = genes[j];
            }
        }

        // 最良コストの遺伝子と比較する
        if (bestCost > minCost) {
            bestCost = minCost;
            bestGene = minGene;
        }

        // 最小・最大コストの遺伝子で確率ベクトルを更新する
        for (int j = 0; j < totalBits; j++) {
            if (minGene[j] == maxGene[j]) {
                probVec[j] = probVec[j] * (1d - learnRate) +
                        (minGene[j] ? 1d : 0d) * learnRate;
            } else {
                final double learnRate2 = learnRate + negLearnRate;
                probVec[j] = probVec[j] * (1d - learnRate2) +
                        (minGene[j] ? 1d : 0d) * learnRate2;
            }
        }

        // 突然変異
        for (int j = 0; j < totalBits; j++) {
            if (rand.nextDouble() < mutProb) {
                probVec[j] = probVec[j] * (1d - mutShift) +
                        (rand.nextBoolean() ? 1d : 0d) * mutShift;
            }
        }
    }
}