2011/10/12 David Griffiths <dave_at_pawfal.org>:
> On Tue, 2011-10-11 at 08:23 +0100, Dan S wrote:
>> For musical interestingness, the idea of the Wundt curve works pretty
>> well - you need to be unpredictable but not too unpredictable since
>> unpredictable is just noise. So one thing you could do for discrete
>> melodies, if they're not too short you could use something like a
>> markov model to predict the later parts of the melody based on the
>> early part. If the trained markov model improves prediction relative
>> to a uniformly-distributed markov model, but not to perfect
>> prediction, maybe that's useful...
>
> So you'd train the markov model on the style of melody you're interested
> in, and the success of a prediction of part of the output is a metric
> for the program that generated it? Sounds good.
That's not what I had in mind but it's another option! Markov models
are quite limited (no overarching structure) so if you trained them on
a particular known style, it'd learn short-range tendencies but not
really longer-range. I think the MM would be more useful for driving
evolution by analysing its results: e.g. for a given population you've
produced, train a markov model on the whole lot, then apply the idea
of the wundt curve - the winning individuals are the ones which the
markov model rates as unpredictable, but not completely unpredictable.
No idea if it'd end up with good results, but that's the magic of
GAs...
Dan
> It would also be possible to generate markov models of betablocker
> programs themselves, as all possible programs are executable - but
> that's something else again :)
>
> cheers,
>
> dave
>
Received on Wed Oct 12 2011 - 06:57:21 BST