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Programming IT Technology

Genetic Algorithm Improves Shellsort 71

gstover writes "As a personal programming project recently, I used a genetic algorithm to find a better sequence of increments for Shellsort. It found at least one sequence that out-performs the best previously known by about 5 percent. Here is an article about it (also here). I believe this is the first time a genetic algorithm has actually improved Shellsort."
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Genetic Algorithm Improves Shellsort

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  • Impressive (Score:2, Interesting)

    by trajano ( 220061 ) on Monday December 09, 2002 @01:25PM (#4845124) Homepage Journal
    Personally, I am quite impressed with the concept of Genetic Algorithms.

    I could imagine your shell sort could be applied to database technologies by creating a separate low priority thread that would generate the "gene pool" to improve the indexing of the tables in the background.

    On a side note I am learning about genetic algorithms with hopes that I may work on it when I take my Masters in Computer Science.
  • Re:Nice and all (Score:4, Interesting)

    by photon317 ( 208409 ) on Monday December 09, 2002 @02:08PM (#4845484)

    There may be some cases where shellsort is more desirable for the exact data being sorted, I don't really know for sure. The importance of this is that he has used a GA to better the optimization work of humans on shellsort. He has laid the groundwork and circumstantial proof out for others to do the same with other algorithms. Of course he evolved a set of constants more than an algorithm itself.

    The next logical place to go with this work, IMHO:

    1) Invent a concise fake machine language for sorting algorithms (a convenience to make the rest of this easier). It should have basic instructions used in manipulating and sorting arrays (move, compare, copy, branching, etc...).

    2) Write a "sort engine" that takes as input algorithms written in your fake language and uses them to sort things (outputting some performance numbers).

    3) Implement every known array sorting algorithm you can find in your little fake machine sort language.

    4) Let a GA evolve the whole algorithm by arbitrarily replacing bytes with other valid bytes from your specialized assembler language, starting with all the known sort algs as parents. Let it run until it stabilizes, using a relatively high mutation rate.

    Of course, the big problem is that if your language implements any kind of looping construct, or any other way that code can be re-executed (and it will almost have to), then you face a "halting problem" when testing each new child. The pratical workaround is of course to know that any reasonable algorithm must finish the sort in a certain bounded amount of cpu cycles, and terminate any children who take longer.

    5) Translate the winning candidate(s) custom machien source back into a generic language like C, and puzzle over exactly why it works so damn well.
  • Re:Nice and all (Score:4, Interesting)

    by Da VinMan ( 7669 ) on Monday December 09, 2002 @02:18PM (#4845566)
    GA is good for a lot of things. For instance, it was used to redesign diesel engines to be more efficient [].

    The big problem with GA though, IIRC, is that the resulting solution is often incomprehensible to a human. I believe Bill Joy did some work with GAs and had comments along those lines (sorry, I couldn't find the quote). Consider for a moment though trying to troubleshoot code generated by a computer. Bad variable names would be just the start of your problems. The logic patterns employed would be essentially random to a human. Many of the patterns would be vestigial and wouldn't even be relevant, but you wouldn't even know that. Identifying the primary execution paths would be a huge chore... never mind actually understanding the basis for why the generated solution actually works.

    How comfortable would you be deploying a solution (hardware or software) where the fundamental design isn't even understood? How the heck do you fix such a thing once it's deployed?
  • Re:Impressive (Score:4, Interesting)

    by jovlinger ( 55075 ) on Monday December 09, 2002 @03:08PM (#4845985) Homepage
    Danny Hillis used his CM-1 to evolve sorting networks for known lengths. Using a predator-prey model (the predator won by generating sequences that the networks failed to sort), he evolved several "optimal" sorters.

    An obvious extension to generic lengths is to use these precomputed networks as recursion base cases for quicksort, instead of switching to selection sort for lengths x (x ~ 5 typically).

    IIRC shellsort works similarly: recursively sorting subsequences and merging results.
  • by Anonymous Coward on Monday December 09, 2002 @05:46PM (#4847960)
    Are you sure the "improvement" is not an artifact of the particular random inputs used? This could easily explain such a small (5%) improvement.

    Also, randomly ordered inputs are not common in practical applications. Did you test (or consider testing) partially sorted and partially reverse-sorted numbers? Presumably the contrived sequences (such as Sedgewick's) are balanced to provide good results for both random and non-random data.
  • Also see comb sort (Score:2, Interesting)

    by flockofseagulls ( 48580 ) on Monday December 09, 2002 @06:14PM (#4848339) Homepage
    The comb sort is a variation of the bubble sort algorithm that achieves N log(N) execution times, similar to heapsort. The algorithm is simple and memory efficient, compared to Quicksort. /c ombsort.htm
  • by gstover ( 454162 ) on Monday December 09, 2002 @06:48PM (#4848761) Homepage

    The only GA-originated increments for Shellsort that I could find online was the one in Crew fall 2001 report [], but that sequence didn't perform very well, & they didn't document their technique except to say that it wasn't a full-blown GA. (They placed some limitations on it. They also mention that Simpson & Yachavaram [] used only a limited GA.)

    I wondered if a GA that fully manipulated bit-strings could produce a sequence as good as Sedgewick's, & I also wanted to document the technique so someone else interested in GAs could implement their own or at least be a little entertained with how it was done.

  • Re:Nice and all (Score:3, Interesting)

    by platypus ( 18156 ) on Tuesday December 10, 2002 @07:23AM (#4853287) Homepage
    But you'd better make damn sure that your GA doesn't improve the algorithm specifically for your "virtual machine". I.e. if pointer arithmetics were very slow (pulling that example out of my ass, btw.), your GA might tend to penalize algorithms using them more often then others.
  • Re:Nice and all (Score:3, Interesting)

    by photon317 ( 208409 ) on Tuesday December 10, 2002 @10:28AM (#4854592)
    True true. Probably the answer would be instead measuring the real execution time in your engine, meaure it in number of various operations, and weight them by how expensive those operations are on a typical modern 32 bit processor.
  • by Sloppy ( 14984 ) on Tuesday December 10, 2002 @01:00PM (#4855565) Homepage Journal
    I think you fundamentally misunderstand what is being done here. Shell Sort works, period. His project does not modify the algorithm in any way. There is no chance of him introducing an error.

    Shell Sort works by roughly sorting, then sorting a little finer, and so on, until you finely sort every piece of data, and hopefully at this final and potentially most time-consuming step, there isn't a lot of work to be done.

    The thing is, there's no platonic God-like perfect mathmatically-proven best choice of how fine or rough the early iterations should be. No matter what increments you use (within certain constraints) Shell Sort will still work every single time (the last step at increment 1 guarantees it) -- it's just a matter of performance. Nobody knows what increments work best, and you can't just mathmatically figure it out.

    ..Which is exactly what makes using a genetic algorithm such a good idea, for trying to come up with some values. GAs are great for optimization problems that defy analysis, as long as you can represent possible solutions as genotype strings and you can write a fitness function to evaluate them. That's what this guy did, and IMHO it is really cool.

  • Re:Nice and all (Score:3, Interesting)

    by doubtless ( 267357 ) on Wednesday December 11, 2002 @09:31AM (#4861895) Homepage
    You might want to check out Genetic Programming instead. It is the method of evolving programs instead of evolving solutions like Genetic Algorithm described here.

    There are some inherit disadvantages of GP, while being much more powerful than GA. One of them being the problem of code growth, usually after some generations, the organisms gets to be too large and starting to cosume too much memory and cpu resources to be feasible. One of the papers I wrote while taking Evolutionary Computation class while in college was the investigation and a 'solution/improvement' to the code growth problem.

My idea of roughing it turning the air conditioner too low.