I am trying to optimize in situations such as the following:

 

Given 100 ability test items with such known item values as (1)
difficulty, (2) correlation with criterion, (3) position in subject
matter taxonomy, (4) illustrated/nonillustrated, (5) abstraction level,
and (6) length, I seek to make three 20-item tests that are as nearly
identical in their properties (difficulty, illustrations, taxonomy, etc)
as possible, using each item only once.  (The goal is to make the tests
interchangeable; there are approx 2.6 e50 such sets of tests.)  I have
an expression for the merit of the extent to which the tests are
identical, but since all of the manipulated variables are binary (i.e.,
each item is "in" or "out" of each of the three tests), derivative-based
methods seem not to apply.  

 

I have read through the optimization chapter in MASS, but those methods
appear not to cover this situation.  Can any of the R optimization
packages handle optimization when the manipulated variables are binary
and numerous?

 

With thanks for any suggestions,

 

Ben Fairbank

Technical Director

Sinclair Customer Metrics

ben@emailsinclair.com


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