> Writing programs on the fly, I got the impression that the relation
> between statistical and deteministic properties of the algorithm gets
> quite relevant. If there is too much randomness, modifying the program
> does not yield any new insight in the process. But this is the case for a
> certain durational range - very fast changes are overlooked easily enough.
> I would guess that gp + ip is really good for these ranges.
>
Exactly, again you return to the issues faced by anyone using generative
processes in live context.
It can become a struggle between signal and noise (or rather the users
signal and the computers evolving signal). Of course it becomes more about
what controls and limitations you put in place to begin with.
As the designer of the basic performance environment you can set yourself up
for an edgey battle, where the
GP aspects can modify high profile elements of the performance, global
tempo, global key etc. Or you
can limit them to evolving lower profile themes such as subtle fx processing
or note masking etc.
However if setup properly having GP strands could allow the performer to
demote some experminetation/development to the computer and increase their
supernatural multitasking skills!
Tom
---------------------------------------------
http://www.nullpointer.co.uk
http://www.r4nd.org
http://www.q-q-q.net
----- Original Message -----
From: "Julian Rohrhuber" <rohrhuber_at_uni-hamburg.de>
To: <livecode_at_slab.org>
Sent: Friday, April 01, 2005 12:01 PM
Subject: Re: [livecode] live genetic programming
>
>>I thought initialy an easy way to construct the
>>fitnnes function would be:
>>Each programm would have a probability of survival. At
>>the end of a turn a random mechanism would decide if a
>>programm would survive to reproduce or die according
>>to the probability..
>>If the programmer does not set a probability of
>>survival to a programm then we can do lots of things
>>like it inherits an average of its parents
>>probabilities...
>>
>>Usualy the fitness function is refered as an important
>>part of the "enviroment" where the indiviuals are
>>living. We could say that in a "live coding gp system"
>>
>>(lcgps(hehehe)) the enviroment would be the
>>music-programmer! He can edit a given proggram, he can
>>kill anyone that's bothering....
>
>
> Generally speaking, I think combining interactive programming with genetic
> programming is interesting because of a certain incongruousness they seem
> to have. GP really comes from the tradition of problem-solving automata,
> interactive programming more from the side of expressing problems. If a gp
> algorithm has found a 'good' solution, it might not express a problem
> clearly, it is still a good solution (and maybe a better one than one
> could have found by thinking about it). A question to you who have a
> certain experience with this 'breeding' practise: can you still follow the
> (textual) output of your algorithms - I mean do they "make sense"? (Of
> course in live ecoding it does not matter so much that we, as a
> programmer, watch the program program, and, just like the audience, don't
> understand what's going on)
>
> Another side of this is that the terminology of genetics (and immunology)
> is full of metaphors with a very political taste, usually neo-liberal:
> competition, survival, defence etc. This terminology also naturalizes
> certain behaviours - like hereditary descendence - which should nearly
> automatically be challenged by a process of live coding. How does
> literature and nature metaphors "interbreed"?
>
>>
>>If a fitness function for scoring music was programmed to score fast
>>melodies
>>highly, the selected individuals would favor speed, and the population as
>>a
>>whole would be slowly filled with programs that create faster melodies. If
>>you
>>could change the fitness function on the fly, your population would react
>>to
>>the changing pressures and adapt further to gain high marks.
>>
>
> Writing programs on the fly, I got the impression that the relation
> between statistical and deteministic properties of the algorithm gets
> quite relevant. If there is too much randomness, modifying the program
> does not yield any new insight in the process. But this is the case for a
> certain durational range - very fast changes are overlooked easily enough.
> I would guess that gp + ip is really good for these ranges.
>
>
>
> What do you think?
> --
>
>
>
>
>
>
>
>
> .
>
>
Received on Fri Apr 01 2005 - 11:19:14 BST