Latin Hypercube Designs with Branching and Nested Factors for Initialization of Automatic Algorithm Configuration

The configuration of algorithms is a laborious and difficult process. Thus, it is advisable to automate this task by using appropriate automatic configuration methods. The $$ method is among the most widely used in the literature. By default, $$ initializes its search process via uniform sampling of algorithm configurations. Although better initialization methods exist in the literature, the mixed-variable (numerical and categorical) nature of typical parameter spaces and the presence of conditional parameters make most of the methods not applicable in practice. Here, we present an improved initialization method that overcomes these limitations by employing concepts from the design and analysis of computer experiments with branching and nested factors. Our results show that this initialization method is not only better, in some scenarios, than the uniform sampling used by the current version of $$ , but also better than other initialization methods present in other automatic configuration methods.