On 6/9/06, Warren Henning <[EMAIL PROTECTED]> wrote:
> Hello everyone,
>
> I've been lurking on this list since it started and haven't yet
> contributed anything, mostly because I'm only a casual Common Lisp
> user, but I have a project idea that I think some of you will find
> interesting. I'm sorry that this email is so long.
>
> I'd like to work on an implementation of various state of the art
> evolutionary computation algorithms, which I'll briefly explain below.
> This library would make Common Lisp the only language to have a
> usable, featureful public implementation of these techniques - for
> once, we could be ahead of the C/C++/Java dorks.
>
> What I'm interested in is a field called evolutionary computation.
> This is a family of optimization techniques which are inspired by
> biology, especially genetics and social insect behavior. Hopefully
> some of you are familiar with this area. Here I'll assume that you
> know what genetic algorithms and genetic programming are. For an
> introduction, see, e.g.,
> http://en.wikipedia.org/wiki/Genetic_algorithm and
> http://en.wikipedia.org/wiki/Genetic_programming . I would try to
> explain them here but it would make the email even longer than it
> already is.
>
> Bill Clementson is interested in doing evolutionary computation in CL
> and has written some blog entries about this, such as
> http://bc.tech.coop/blog/040619.html .
>
> Genetic programming (GP) as it is commonly used is not perfect. In
> classic Koza-style GP, you have no control over what calls what (i.e.,
> no control over the parameters which functions in your function set
> are given when constructing trees), so usually you just set things up
> so that all your functions take and receive the same datatype, usually
> float/double. There have been some variants of this, such as strongly
> typed GP, which attempt to allow you to restrict the search space. But
> you still don't really have fine-grained control over the kind of
> solutions you evolve, making the search space enormously large and
> making it somewhat difficult to incorporate problem-specific domain
> knowledge into your GP system.
>
> There's a relatively new evolutionary technique/algorithm which is a
> big leap forward in this regard. It's called grammatical evolution
> (GE). It was developed in 1998. See
> http://www.grammatical-evolution.org/pubs.html#1998 for a few
> different introductions. The basic idea is to separate genotype and
> phenotype. The "genotype" is a list of integers. This is mapped to
> selecting rules from a Backus-Naur form grammar you supply for the
> problem. The papers at grammatical-evolution.org explain precisely how
> this is done. But basically it allows you to custom-tailor the evolved
> solutions any way you want and to productively restrict the search
> space by altering the grammar in use. GE is becoming more popular and
> has been applied to things like predicting stock indices, predicting
> corporate bond ratings, predicting corporate bankruptcy, training
> artificial neural networks, and so on. Like all evolutionary methods,
> it can be easily parallelized.
>
> Implementations of GE are hard to come by. At the present time, all
> that exists publicly is a partial implementation of GE in C++ called
> libGE (see the link to libGE at www.grammatical-evolution.org) but its
> documentation is confusing as hell and you still have to use a
> separate genetic algorithm library to do the actual searching,
> requiring that the user learn and use two complicated libraries to get
> a single task done. To me, it is useless.
>
> I propose that an easy-to-use library implementing full-blown GE be
> implemented. Lisp is a great environment for prototyping things like
> GE; that's why GP was first developed and used in Common Lisp. I have
> a lot of other ideas for what could be done, such as implementing
> swarm intelligence algorithms.
>
> Any takers?
>
> Cheers,
> Warren Henning
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Yes I would definitely be interested in working on this in some capacity.

Justin
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