Description
From PeterVermont on December 21, 2012 18:00:55
I would email but don't know your email address.
I am the main developer for MDR - Multifactor Dimensionality Reduction which is a fairly simple classifier used in bioinformatics. See http://sourceforge.net/projects/mdr/ Normally, users do an exhaustive search over attribute combinations but we also have a random timed mode. Currently I make no effort from to prevent the random from testing the same combination more than once. I would like a tool that would allow me to get combinations without replacement. The issue is that it must be extremely efficient and fast -- it is not worthwhile to just maintain a list of tested combinations. It would be okay and perhaps even preferable if the progression were 'psuedo-random' in such a way that all attributes were tested fairly equally.
Another related, but harder problem, is using evolutionary algorithms to test combinations. I have found that this tends to test the same combinations many many times, since I already use elitism so I don't lose track of winners, this is a big waste of processing time. If I had a good way to know what has been sampled previously I could prevent waste -- this would also act as a form of 'novelty' seeking which has been shown in some evolutionary algorithm contexts to be helpful.
Thanks,
Peter Andrews
Norich, Vermont USA
Original issue: http://code.google.com/p/combinatoricslib/issues/detail?id=6