very simple Genetic Algorithm completed

Matthew_WARREN at Matthew_WARREN at
Fri Feb 1 16:11:02 CET 2008

On Jan 31, 10:43 am, Matthew_WAR... at 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
> > 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, 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.


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