Time-Bounded Sequential Parameter Optimization

The optimization of algorithm performance by automatically identifying good parameter settings is an important problem that has recently attracted much attention in the discrete optimization community. One promising approach constructs predictive performance models and uses them to focus attention on promising regions of a design space. Such methods have become quite sophisticated and have achieved significant successes on other problems, particularly in experimental design applications. However, they have typically been designed to achieve good performance only under a budget expressed as a number of function evaluations (e.g., target algorithm runs). In this work, we show how to extend the Sequential Parameter Optimization framework [SPO; see 5] to operate effectively under time bounds. Our methods take into account both the varying amount of time required for different algorithm runs and the complexity of model building and evaluation; they are particularly useful for minimizing target algorithm runtime. Specifically, we avoid the up-front cost of an initial design, introduce a time-bounded intensification mechanism, and show how to reduce the overhead incurred by constructing and using models. Overall, we show that our method represents a new state of the art in model-based optimization of algorithms with continuous parameters on single problem instances.