Hrvoje Horvatic wrote:
and I don't like using "blackbox" approaches that I don't FULLY understand and can't "tweak" to my needs... what I want is to "tweak", not just "plug-and-play"... I checked that NOMAD stuff you suggested, but didn't "get it" at first, and I'm at home with GA, soooooo...
Well, NOMAD is open source and has some tweakable parameters. But as for understanding it .. it is based on some fairly complex math. Basically it samples the search space, builds an internal multi-dimensional model, and uses that model to determine what points to sample next. This is a well-established technique based on recent research, but there are a lot of variant approaches.
The reason it is called a "black box" optimizer is not that the internals are opaque, but because unlike some algorithms, it does not require the function being optimized to have a mathematical form that allows computation of its derivative. So the function can be the outcome of a simulation process, for example. This kind of algorithm generally assumes the objective function is expensive to compute, so tries to minimize the number of evaluations required.
How did it all end up for you with Arasan? If I recall correctly, you had some problems with getting good results for eval function?
No, the eval function approach did not work for me, still doesn't. NOMAD has enabled me to make some progress. However. the problem I am having recently is that I have been trying to tune some variables that have small effects, and this is inherently difficult. Optimization software does not really work for that: you need to have knobs to turn that do something significant, otherwise it will just bounce around randomly.
--Jon