GeneticAlgorithm

int GeneticFitnessFunction(node state) {
    return MAX_COST - CalculateAttackingQueens(state);
}

node GeneticFindBestState(vector<node>& population) {
    node best = population[0];
    int bestScore = GeneticFitnessFunction(best);

    for (unsigned i = 1; i < population.size(); i++)
    {
        int score = GeneticFitnessFunction(population[i]);
        if (score > bestScore) {
            best = population[i];
            bestScore = score;
        }
    }

    return best;
}

void GeneticCreatePopulation(vector<node>& population) {
    // TODO Implement this (InClass #7)
}

node GeneticSelect(vector<node>& population) {
    float probability = 1.f;
    int score = 0;

    int i = rand() % sizeof(population);

    for (; true; i = rand() % sizeof(population)) {
        score = GeneticFitnessFunction(population[i]);
        probability = (float)(score / GENETIC_MAX);
        if (probability >= (float)((rand() % 101) / 100.f)) {
        }
    }

    return population[i];
}

node GeneticReproduce(node x, node y) {
    node child = x;

    for (int i = rand() % (sizeof(x) / sizeof(node)); i < (sizeof(x) / sizeof(node)); i++) {
        child.queens[i] = y.queens[i];
    }

    return child;
}

node GeneticMutate(node child) {
    // TODO Implement this (InClass #7)
    return child;
}

// TODO (InClass #7 - Implement genetic algorithm)
node GeneticAlgorithm() {
    node test = {0, 1, 2, 3, 4, 5, 6, 7};
    return test;
}