I'm experimenting with different search algorithms to investigate the parameter space.
Would any systems developers like to share some of their own experiences/insights/techniques with respect to this issue?
Parameter Search Algorithms
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Parameter Search Algorithms
Last edited by shakyamuni on Sat Jan 08, 2005 6:00 pm, edited 1 time in total.
Large Optimizations
Here are some questions that come to mind, and how I have approached similar situations.
What I've just done is partition a larger problem into a set of smaller problems by introducing a constraint on the solution. Now I can use a number of techniques to look for local optima within each partition. If you think of parameter optimization as overlying a grid on a range of possible values, the first step is to open up the grid size (or parameter increment). Then, when I find values on this grid that are performant, I will recursively refine the search with a finer grid size, but only on a few selected areas (using a smaller parameter increment).
This type of approach works for parameters you can increment (like channel breakout length). It does not work for things like portfolio market selection. In that case, what I will take random samples from the result space, and examine distribution of the aggregate results (profits, draw downs, etc). The aggregate won't give me an optimal selection, but it will indicate the consistency of the sample, and I can refine my next step based on those results.
In both cases, I've introduced techniques to prune the sample space so that is becomes feasible and efficient to search for results.
Cheers,
Kevin
- What characteristics do I want in an optimal solution?
Is is nessecary to examine the entire problem space?
Can I sample the problem space? What conclusions can I draw from the aggregate sample?
What I've just done is partition a larger problem into a set of smaller problems by introducing a constraint on the solution. Now I can use a number of techniques to look for local optima within each partition. If you think of parameter optimization as overlying a grid on a range of possible values, the first step is to open up the grid size (or parameter increment). Then, when I find values on this grid that are performant, I will recursively refine the search with a finer grid size, but only on a few selected areas (using a smaller parameter increment).
This type of approach works for parameters you can increment (like channel breakout length). It does not work for things like portfolio market selection. In that case, what I will take random samples from the result space, and examine distribution of the aggregate results (profits, draw downs, etc). The aggregate won't give me an optimal selection, but it will indicate the consistency of the sample, and I can refine my next step based on those results.
In both cases, I've introduced techniques to prune the sample space so that is becomes feasible and efficient to search for results.
Cheers,
Kevin
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Thanks for your comments.
Last edited by shakyamuni on Sat Jan 08, 2005 5:57 pm, edited 1 time in total.
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I use plain ordinary SA. The problem with ASA is, nobody can get it to work except Lester Ingber
http://portal.acm.org/citation.cfm?id=2 ... coll=GUIDE
It's worth reading the Corana et al article, just to have a look at their multimodal test function. Specially designed to give deterministic algorithms apoplexy!
http://portal.acm.org/citation.cfm?id=2 ... coll=GUIDE
It's worth reading the Corana et al article, just to have a look at their multimodal test function. Specially designed to give deterministic algorithms apoplexy!