🚀 go-pugleaf

Recently, Shumeet Baluja announced on this list the availability of
CMU-CS-97-107, "Using Optimal Dependency-Trees for Combinatorial
Optimization: Learning the Structure of the Search Space" by Baluja
and Davies.

That paper discusses and extends some work presented in "MIMIC:
Finding Optima by Estimating Probability Densities" by De Bonet,
Isbell, and Viola (to appear in NIPS*96).

It seems worthwhile to mention that this paper is also now available:

	ftp://ftp.ai.mit.edu/pub/isbell/mimic.ps.gz

Abstract

In many optimization problems, the structure of solutions reflects
complex relationships between the different input parameters. For
example, experience may tell us that certain parameters are closely
related and should not be explored independently.  Similarly,
experience may establish that a subset of parameters must take on
particular values. Any search of the cost landscape should take
advantage of these relationships. We present MIMIC, a framework in
which we analyze the global structure of the optimization landscape.
A novel and efficient algorithm for the estimation of this structure
is derived. We use knowledge of this structure to guide a randomized
search through the solution space and, in turn, to refine our estimate
of the structure.  Our technique obtains significant speed gains over
other randomized optimization procedures.


Peace.