Conditional Generative Representation for Black-Box Optimization with Implicit Constraints

Black-box optimization (BBO) has become increasingly relevant for tackling complex decision-making problems, especially in public policy domains such as police districting. However, its broader application in public policymaking is hindered by the complexity of defining feasible regions and the high-dimensionality of decisions. This paper introduces a novel BBO framework, termed as the Conditional And Generative Black-box Optimization (CageBO). This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision …

application become box complexity constraints cs.ce cs.lg decision decisions dimensionality domains framework generative making novel optimization paper police policy policymaking public public policy representation stat.ml