Highest Utility First Search: a Control Method for Multi-level
Stochastic Design
Paper
(32 pages, 500kb Postscript)
Louis Steinberg,
J. Storrs Hall, and
Brian D. Davison
March 1998
Abstract
An intrinsic characteristic of stochastic optimization methods, such as
simulated annealing, genetic algorithms and multi-start hill climbing, is
that they can be run again and again on the same inputs, each time potentially
producing a different answer. When such algorithms are used in a design
process with multiple levels of abstraction, where the output of one stochastic
optimizer becomes the problem statement for another stochastic optimizer,
we get an implicit tree of alternative designs. After each optimizer run we
face a control problem of which level's optimizer to run next, and which
design alternative to run it on. This problem is made more difficult by the
fact that we generally can get a precise evaluation of the design alternatives
only at the lowest level (the final results), and must make do at higher levels
with only an estimate of how good a final design each alternative will lead to.
HPCD Technical Report #59, Department of Computer Science, Rutgers University.
Back
to Brian Davison's publications
Last modified: March 25, 1998
Brian D. Davison