In addition to BO, there are other global optimization algorithms, each with strengths and weaknesses. A non-exhaustive list of popular global optimization schemes, in order of typically increasing efficiency and computational complexity is given: manual tuning, grid search, random sampling, Sobol sampling, genetic algorithms, and BO. For inexpensive evaluations (hundreds of thousands of evaluations), random or Sobol sampling is typically preferred. For moderately expensive evaluations (tens of thousands of evaluations), genetic algorithms and scalable BO are typically preferred. Finally, for expensive-to-evaluate functions (hundreds to thousands of evaluations), BO is typically preferred. Exact BO scales poorly with dataset size, for which variational Gaussian Process models or scalable surrogate models may be used instead . Likewise, for its straightforward implementation and low computational requirements, quasi-random sampling is preferred for large datasets. Grid-based searches in high dimensional spaces tend to be inefficient due to systematic sparse regions in the center of hyperboxes that make up the high-dimensional grid. Manual tuning by humans can often lead to local optimization and inefficient searches.