I just had some crazy moment of inspiration while driving back to school the other day on how to factor numbers really quickly; I thought about implementing evolutionary computation to converge on a factor, I’ll go into the details if anyone really wants to hear about it.

Does anyone know if there has been any algorithms already invented that use genetic algorithms to factor numbers or anything similar? I’ve been searching papers online on both subjects, but I doubt that Google would be a terribly accurate indicator of the research done on this subject.

Anyway, I wrote a little program in Java that tests this out. Terribly slow, and I think it still may be NP, and way behind GNFS; I haven’t done much work on it because classes started up again, but I have some random trials I did:

Time to factor n-bit number:
n=24: 0.802s
n=28: 4.667s
n=32: 16.734s
n=36: 57.693s

IANA computer scientist, mathematician or geneticist, but I think that to implement a genetic algorithm, you need a non-Boolean definition of the fitness for whatever is the analog of genes in your program.

If your gene analog is a trial factor here, surely either it is a true factor, or it isn’t? This reduces your genetic algorithm to simple computational tests.

The only alternative I can think is if you are taking sets of trial factors as your genes, and have found some way to quickly assess the likelihood that your set contains an actual factor without testing all its elements. If you have, then the idea could have potential…

Well to answer your last question, I don’t see it as very practical with today’s computing speeds. I’m able to factor a 47 bit number on a program I made a while back in 1.4 seconds. And that’s on an Athlon 600 Mhz. There really aren’t many purposes to factor larger numbers.

I used an alternative representation of integers by expressing them as ab+c where a, b, and c are n/4-bit numbers (which is why all my trial runs are with integers of 4n bits). Genes were a, b, and c successively in base-2. Then wrote the fitness furiction as the fractional part when the number to be factored was divided by ab+c. Array of genes was sorted by fitness and mating selection was done by quadratic selection.

Once the fractional part reaches 0, you have a factor.

I actually don’t know completely why this works. I realized that after writing this, that the genetic algorithm probably really didn’t do anything, because crossovers really wouldn’t have an effect on the fitness at all. However, compared to random search, it does vastly better, probably because there are exponentially more representations of each integer under this. Evolutionary computation probably isn’t the best way to go, and the code is far from optimized; I was just wondering if anyone had done anything like it before.

In addition, I better expand my question: does anyone know how to determine complexity when there is a randomness involved?

There are complexity classes for decision problems that can be solved with probabilistic Turing machines. PP is probabilistic polynomial time. BPP is bounded probabilistic polynomial time. PP’s not terribly useful (NP is contained in it), but BPP is likely equal to P (contingent on the existence of strong pseudorandom number generators). Papadimitriou has a whole chapter on randomized computation.

Peter Shor discovered a polynomial-time algorithm for factoring on a quantum computer. That puts the factoring problem in the quantum computation class BQP, which is contained in PP and contains BPP. Shor’s algorithm at Wikipedia

You can express the running time/space per generation as a function of the input size. That’s probably as close as you can come to a meaningful notation of complexity (but I haven’t read Papadimitriou, so you might want to check that out).

Here’s another question: how do you show correctness?

Basically, if you can find a relatively quick way to factor the products of two extremely large primes, you’ve undermined the basis of most modern cryptography.

A laptop running quicksort can beat a Cray running bubblesort when both are sorting a sufficiently long list of numbers. That’s a standard example, too, and one that any reasonably competent programmer should be familiar with.

More processing power isn’t the answer. Better algorithms are.

Both of those algorithms aren’t affected by how close to sorted the original list is. The standard implementation of quicksort (pick a pivot element, any pivot element) is, and runs very slowly on a nearly-sorted list. There are other schemes for picking a pivot element, but most just decrease the probability of a bad one.

I’ve never seen data on quicksort modified to use a linear-time median selection for picking its pivot. It would be interesting to see how that compares to some of the other algorithms out there.

(Regarding Mergesort and Bubblesort on nearly sorted data. Standard CS textbook versions.) Mergesort will sort in about half its worst-case time on nearly sorted data. Bubble sort approaches (and can reach) linear.

The first linear time median algorithms had constants above 7 IIRC. The most recent algorithm I’ve seen I believe has a constant near 2. All are bears to program and can cause horrible page thrashing on large sets. The random choice is still better on average. But, you don’t have to worry about that quadratic worst-case if you’re the fretful type.

As to the data posted in the OP: The pattern is clearly too small to prove anything, but it definitely seems to be doubling for every bit added. I.e., hardly a breakthrough algorithm in performance terms. But I think such ideas are worthy of further research.

What textbooks are you using? Every implementation of bubblesort I’ve ever seen will examine O(n[sup]2[/sup]) array entries. Take this typical implementation:

void bubble_sort(long thearray[], int sizeofarray)
{
int i,j;
long temp;
for(i=sizeofarray; i>=0; --i) // For each spot in the array
{
// (starting at the top)
for(j=0; j<i; j++) // Walk through the array to that
{
// spot
if(thearray[j] > thearray[j+1]) // At each location, compare the
{
// two adjacent elements
temp = thearray[j];
// Swapp elements as needed
thearray[j] = thearray[j+1];
thearray[j+1] = temp;
}
}
}
return;
}

That was more a curiosity. I’ve never seen anything on the performance of quicksort with a guaranteed good pivot. Doesn’t sound like it’s all that great, which doesn’t surprise me.

Can’t be worse to code than a linear time in-place merge, though.

You can add a termination condition to exit the outer loop if you make it through the inner loop without swapping any entries. Here is one simple example (the inner loop needlessly runs through all n entries each time, though; that URL also has the strangest choice of less-than symbol I’ve ever seen).

Ah, I see it now; I didn’t scroll down far enough. I’d still call it a bubblesort, though, and it’s O(n) on a sorted list.

Oh, I know. But it’s an arbitrary total ordering, so it should be antisymmetric. \otimes is far too symmetric a symbol to be used for an antisymmetric relation, to my sense of aesthetics.

I don’t disagree. On the other hand, it might be interesting to think about what happens to various sorting algorithms if < is replaced by an arbitrary and not necessarily antisymmetric relation.