For fully-continuous parameter problems, the gradient-based acquisition optimization makes use of the BoTorch library, which uses the SLSQP algorithm. For highly constrained problems (i.e. those for which a significant fraction of the parameter space is infeasible), we found that the SLSQP optimizer undergoes serious convergence issues. Ultimately, this significantly increases the overall per-iteration runtime of the optimization procedure. With our in-house approach for constrained acquisition optimization using DEAP, we are able to preserve optimization performance while significantly reducing the per-iteration runtime. Empirical comparisons of the two methods are shown in Fig. and in Table .1 A 2. We use the 2d (un)constrained StylinskiTang surface, testing the FCA strategy with t = {0.2, 0.5, 0.8} for the constrained case. While the genetic acquisition optimizer is slightly slower in the unconstrained case, constrained acquisition optimization only requires ≈ 10 seconds per iteration, while the gradient optimizer can take as long as 250 seconds per iteration. Thus, we elect to use the DEAP acquisition optimization strategy for all experiments in this paper.