  Bayesian Optimization (BO) is a data-efficient method for global black-box
optimization of an expensive-to-evaluate fitness function. BO typically assumes
that computation cost of BO is cheap, but experiments are time consuming or
costly. In practice, this allows us to optimize ten or fewer critical
parameters in up to 1,000 experiments. But experiments may be less expensive
than BO methods assume: In some simulation models, we may be able to conduct
multiple thousands of experiments in a few hours, and the computational burden
of BO is no longer negligible compared to experimentation time. To address this
challenge we introduce a new Dimension Scheduling Algorithm (DSA), which
reduces the computational burden of BO for many experiments. The key idea is
that DSA optimizes the fitness function only along a small set of dimensions at
each iteration. This DSA strategy (1) reduces the necessary computation time,
(2) finds good solutions faster than the traditional BO method, and (3) can be
parallelized straightforwardly. We evaluate the DSA in the context of
optimizing parameters of dynamic models of microalgae metabolism and show
faster convergence than traditional BO.
