  In many experimental settings, one is tasked with obtaining information about
certain relationships by applying perturbations to a set of independent
variables and noting the changes in the set of dependent ones. While
traditional design-of-experiments methods are often well-suited for this, the
task becomes significantly more difficult in the presence of constraints, which
may make it impossible to sufficiently excite the experimental system without
incurring constraint violations. The key contribution of this paper consists in
deriving constraint back-off sizes sufficient to guarantee that one can always
perturb in a ball of radius $\delta_e$ without leaving the constrained space,
with $\delta_e$ set by the user. Additionally, this result is exploited in the
context of experimental optimization to propose a constrained version of G. E.
P. Box's evolutionary operation technique. The proposed algorithm is applied to
three case studies and is shown to consistently converge to the neighborhood of
the optimum without violating constraints.
