Matthew_WARREN
2/1/2008 3:11:00 PM
On Jan 31, 10:43 am, Matthew_WAR...@bnpparibas.com wrote:
> > Hi,
> >
> > I got some help with this from here, and there's been a little bit of
> > discussion around GA's recently, so thought I'd post up my likey slow
and
> > clunky version of a GA that in essence just 'evolves' a solution to
'make a
>
> Some improvement tips:
>
> 0. Tack this bit onto the end of quickga.py, and you wont have to
> write a separate routine to import quickga and invoke evolve():
>
> if __name__ == "__main__":
> evolve()
I hear you, but something I dont tend to do as I use pythons interactive
prompt a lot, and I like things not autorunning when I import them. I
normally add it in at the end of writing something.
> 1. Remove all calls to floatify, and instead start your program with:
> from __future__ import division
> On my system this gained about 16% improvement.
>
I was sure there must be a nicer way of achieving that, thanks :)
>
> 2. Bugfix: In 2 places, change:
> newgene=genes[randint(0,14)-1]
> to
> newgene=genes[randint(0,14-1)]
>
> randint(a,b) returns values from a to b inclusive, and genes is a list
> containing 14 elements (so you want subscripts from 0 to 13). (Usingd
> subscripts from -1 to 13 will bias the selection of genes to use twice
> as many of the last item in the list, since both -1 and 13 will give
> that value.)
>
> Similarly, change:
>
> s=rationalise_signs(''.join([ genes[randint(0,len(genes))-1] ...
> to:
> s=rationalise_signs(''.join([ genes[randint(0,len(genes)-1)] ...
>
Doh! - I was assuming I would get 1-14, not 0-14 with randint. Brain slip.
>
> 3. Change mutate to build a list instead of a string. Then use
> ''.join() at the end to convert the list into a single string return
> value.
>
Interesting. I knew string operations arent necesarilly the quickest but I
didnt think of lists as being any quicker because I'm sure I've read
somewhere pythons strings are treated like lists, and I assumed the
''.join() would undo any savings. I'll go read up on it :)
>
>
> (This was less of a speed improvement than I thought it would be, but
> IIRC, the optimization of successive string concatentions is only
> available when running Python on Windows. If you are running on Linux,
> this should have more benefit.)
It is windows.
>
>
> 4. Include psyco to cut execution time in half. Put this at the top
I was doing this as your mail arrived :)
>
>
> 5. On a hunch that many of your individuals are the same string, I
> lifted the call to eval out of express() into a separate routine
> called evaluate(), and added a memoizing cache to skip the call to
> eval if the string had been eval'ed before - if so, the value is
> reported from the cache. This chopped another 40% off the runtime.
>
> evalcache = {}
> def evaluate(s):
> if s in evalcache:
> ret = evalcache[s]
> else:
> ret = eval(s)
> evalcache[s] = ret
> return ret
>
> (A note on timing this code: since there are many calls to
> randomization methods, consistent timing requires an explicit call to
> random.seed() to ensure that a consistent set of random numbers is
> used. Otherwise, the timing gets thrown off by the randomization,
> which sends the program down different paths.)
Thats great! not something I would have thought of, caching the eval
results. Simple and effective :)
>
>
> 6. This is another change that had little effect, but I'll at least
> put it out there (a leftover from an algorithmic optimization course I
> took ages ago). Instead of:
>
> fitness=abs(fitvalue-expression)
>
> try using:
>
> fitness=(fitvalue-expression)*(fitvalue-expression)
>
I originally had this. I was seeing absoloutley huuge int's appear and
wondered if dealing with those in memory might slow things down. I'll try
it either way.
...timing the code. I was just puzzling over that, and yep a call to seed()
will fix it.
Thanks for the suggestions and hints, esp the cache idea.
Matt.
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