The Search Space Toolkit (SST) is a suite of tools for investigating t
he properties of the continuous search spaces which arise in designing
complex engineering artifacts whose evaluation requires significant c
omputation by a numerical simulator. SST has been developed as part of
NDA, a computational environment for (semi-)automated design of jet e
ngine exhaust nozzles for supersonic aircraft which resulted from a co
llaboration between computer scientists at Rutgers University and desi
gn engineers at General Electric and Lockheed. Though the design space
s for this sort of engineering artifact are mainly continuous, they ty
pically include features such as unevaluable points, multiple local op
tima, and large derivatives which cause difficulties for standard nume
rical optimization methods. The search spaces which SST explores also
differ significantly from the discrete search spaces that typically ar
ise in artificial intelligence research, and properly searching such s
paces requires a synergistic combination of numerical methods and AI t
echniques and is a fundamental AI research area. By promoting the desi
gn space to be a first class entity, rather than a ''black box'' burie
d in the interface between an (unconstrained) optimizer and a simulato
r, SST allows a more principled approach to automated design.