Impact of Initial Tuning for Algorithm That Solve Query Routing

The algorithms are the most common form of problem solving in many science fields. Algorithms include parameters that need to be tuned with the objective of optimizing its processes. This work uses Hoeffding race techniques, with the objective to obtain the best initial combination of variables to use it as an input configuration. Hoeffding race quickly discard less promising candidates as soon as there are evidences enough to remove them from the competition. These evidences are based on the use of any statistical test that, at a given confidence level, would set a range of expected performance for configuration. All the experiment was applied in AdaNAS (Adaptive Neighboring-Ant Search), an algorithm that was developed to route queries through the Internet. Our results show that there is a significant gain in efficiency of the AdaNAS algorithm by using the simple, but powerful, technique of initial setting of parameters presented in this paper. In our experiments, the average efficiency was improved 50% by using a good initial configuration.