  When faced with a limited budget of function evaluations, state-of-the-art
black-box optimization (BBO) solvers struggle to obtain globally, or sometimes
even locally, optimal solutions. In such cases, one may pursue solution
polishing, i.e., a computational method to improve (or ``polish'') an incumbent
solution, typically via some sort of evolutionary algorithm involving two or
more solutions. While solution polishing in ``white-box'' optimization has
existed for years, relatively little has been published regarding its
application in costly-to-evaluate BBO. To fill this void, we explore two novel
methods for performing solution polishing along one-dimensional curves rather
than along straight lines. We introduce a convex quadratic program that can
generate promising curves through multiple elite solutions, i.e., via path
relinking, or around a single elite solution. In comparing four solution
polishing techniques for continuous BBO, we show that solution polishing along
a curve is competitive with solution polishing using a state-of-the-art BBO
solver.
