Someone has a subproject for a solver framework, see the taseree project
at sourceforce, when it has matured it will become part of the main
release in the drools-solver project directory. I'm sure Geoffrey would
love any additional help. I already plan to extend this for Genetic
Algorithms, as that is my academic background.
Mark
Ellen Zhao wrote:
Hi folks,
I used to buy some hardware from here and played with their configurator:
http://www.alternate.de/html/includeStaticBig.html?treeName=KONFIGURATORE...
(The page is in German but they have graphic icons around it's not
hard to understand....)
If you choose an amd based motherboard and an Intel CPU together,
their web configuration will warn you and you have to re-choose.
I do not really know how they implemented it but I thought a
rule-based implementation is very suitable for such kind of thing.
I ran into a combinatorial optimization problem recently. At the first
thought, any Rete-based rule engine is not really the right tool for
searching optimal solutions in a *huge* solution space. But when I
started to implement my algorithm, it turns out that Drools can still
possibly help me in two places. I decided to use Monte Carlo
optimization algorithms, for my problem the most interesting
candidates are evolution strategy and genetic algorithms. Before I
start to randomly poll elements from the solution space, I could use
drools to efficiently filter out the absolutely unwanted elements
(this has its drawbacks too...depends on how tolerate to the end
result the end users *want* to be). My filter condition changes very
often (it would depends on the things like personal preference of a
particular user, etc), so using drools for filtering makes sense. The
filtering does effectively reduce the solution space. And then, drools
could be used to calculate the global score after each poll from the
solution space. If I use the genetic algorithm, the mixing rules could
also be defined in a .drl file (which will be very easy to change, and
yes, it needs frequent changes when I want to tune the system). The
gene mixing could also be running in Drools' working memory. There
might be some performance gain against straight iteration + data
manipulation in a single java procedure. But I have not implemented it
yet (will start coding from tomorrow), so far not sure about the
performance gain. if anything turns out good, I'd be glad to report.
Regards,
Ellen N. Zhao
On 7/18/07, Dr. Gernot Starke <gs(a)gernotstarke.de> wrote:
> Hi Drools-users,
>
> to further promote JBoss-Drools I suggest to compile a few practical
> application scenarios or case-studies.
>
> A few of you gave hints on your application domains as answer to "Re:
> [rules-users] Entreprise using Drools with success".
>
> I am most willing to document examples, as long as you provide enough
> details (plus sample rules...).
>
> What about a section in the JBoss-Drools-Wiki?
>
> I'll start writing a sample one of these days - so far see my first
> ideas on content-based routing:
>
http://rbs.gernotstarke.de/samples/samples/routing.html
>
> feedback welcome
> Gernot
>
>
>
>
> _______________________________________________
> rules-users mailing list
> rules-users(a)lists.jboss.org
>
https://lists.jboss.org/mailman/listinfo/rules-users
>
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