Considerations of Budget Allocation for Sequential Parameter Optimization (SPO)

Obviously, it is not a good idea to apply an optimization algorithm with wrongly specified parameter settings, a situation which can be avoided by applying algorithm tuning. Sequential tuning procedures are considered more efficient than single-stage procedures. [1] introduced a sequential approach for algorithm tuning that has been successfully applied to several real-world optimization tasks and experimental studies. The sequential procedure requires the specification of an initial sample size k. Small k values lead to poor models and thus poor predictions for the subsequent stages, whereas large values prevent an extensive search and local fine tuning. This study analyzes the interaction between global and local search in sequential tuning procedures and gives recommendations for an adequate budget allocation. Furthermore, the integration of hypothesis testing for increasing effectiveness of the latter phase is investigated.