In this work, a reducible search space is a search space that exhibits identical solutions for different parameterizations that can be collapsed to a single solution and a single parameterization (i.e. an irreducible search space) through reparameterization or imposition of constraints. Baird et al. found that mapping symmetrically related sets of parameters to an irreducible representation (i.e. a fundamental zone in crystallographic terms ) exhibited distinct advantages related to accuracy and compu-tational efficiency of distance calculations. Similar to the crystallographic representation, reducibility in this work focuses on leveraging domain knowledge about the relationship between input parameters of an otherwise "black-box" objective function to restrict the search space through reparameterization. Examples are a set of parameters that is represented as a composition or formulation (i.e. Al 2 O 3 ≡ 0.4Al + 0.6O where 0.4 + 0.6 = 1.0) (referred to as "comp") or a set of parameters that exhibits permutation invariance (e.g. (referred to as "order"). When no additional parameter constraints other than lower and upper limits are used, we refer to this as "Bounds-only".