A neuroevolution program I am in the process of debugging does not produce random values every time it is called. In the program, a vector of Network objects are initialized with the following statement:

vector population(POPULATION_SIZE, Network(sizes, inputCount));

Why I believe the program not to be converging to an optimal solution is that, always, the first 100 of the population are the same. When a network is initialized in this manner, the connection weights and neuron biases are (each) initialized with the following class function:

double Network::randDouble(double low, double high) {
    /*default_random_engine generator(std::chrono::system_clock::now().time_since_epoch().count());
    uniform_real_distribution distribution(low, high);
    return distribution(generator);*/

    /*srand(time(NULL));
    double temp;
    if (low > high) {
        temp = low;
        low = high;
        high = temp;
    }
    temp = (rand() / (static_cast(RAND_MAX) + 1.0)) * (high - low) + low;
    return temp;*/

    /*mt19937 rgn(std::chrono::system_clock::now().time_since_epoch().count());
    uniform_real_distribution gen(low, high);
    return gen(rgn);*/

    default_random_engine rd;
    uniform_real_distribution gen(low, high);
    auto val = std::bind(gen, rd);
    return val();
}

The 3 commented-out sections are previously attempted means of generating the functionality required. In each case, they produce the same numbers for each network (differing from 1 weight to another, but not 1 network to another). The methods attempted are based on answers from here:

1. c++-default_random_engine creates all the time same series of numbers
2. http://en.cppreference.com/w/cpp/numeric/random/uniform_real_distribution

In addition, the second method produces the same results with or without the seed. I must be missing something.

Another, albeit potentially irrelevant concern, is that functions using this may be parallel-ized using OpenMP, and that when called in parallel, the results could be the same.