While some optimization strategies can perform well on a wide range of problems, selection of the best strategy for a novel problem remains non-trivial. In part, this is due to the structure of the objective function being a priori unknown, which renders comparison to related problems challenging. As such, the selection of strategies for a novel problem currently relies heavily on user preference, the availability of user-friendly optimization software, and heuristics, as opposed to empirical evidence. In addition, benchmarking optimization strategies on realworld experimental problems is often prohibitively expensive as a result of the cost associated with experimental measurements. Thus, benchmarks continue to be carried out using analytical functions. While these benchmarks are convenient, given that their values can be computed instantaneously, they often do not constitute realistic optimization tasks encountered in real-world applications.