---- abstract ----

Learning When Reformulation is Appropriate for Iterative Design

Mark Schwabacher
Thomas Ellman
Haym Hirsh
Gerard Richter

Department of Computer Science
Rutgers University
New Brunswick, NJ 08903
{schwabac, ellman, hirsh, richter}@cs.rutgers.edu

HPCD-TR-28

It is well known that search-space reformulation can improve the speed
and reliability of numerical optimization in engineering design.  We
argue that the best choice of reformulation depends on the design
goal, and present a technique for automatically constructing rules
that map the design goal into a reformulation chosen from a space of
possible reformulations.  We tested our technique in the domain of
racing-yacht-hull design, where each reformulation corresponds to
incorporating constraints into the search space.  We applied a
standard inductive-learning algorithm, C4.5, to a set of training data
describing which constraints are active in the optimal design for each
goal encountered in a previous design session.  We then used these
rules to choose an appropriate reformulation for each of a set of test
cases.  Our experimental results show that using these reformulations
improves both the speed and the reliability of design optimization,
outperforming competing methods and approaching the best performance
possible.

(To appear in Workshop of Machine Learning in Engineering at the 14th
International Joint Conference on Artificial Intelligence, Montreal,
August, 1995.)