Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

ReACTR: Realtime Algorithm Configuration through Tournament Rankings / 304
Tadhg Fitzgerald, Yuri Malitsky, Barry O'Sullivan

It is now readily accepted that automated algorithm configuration is a necessity for ensuring optimized performance of solvers on a particular problem domain. Even the best developers who have carefully designed their solver are not always able to manually find the best parameter settings for it. Yet, the opportunity for improving performance has been repeatedly demonstrated by configuration tools like ParamILS, SMAC, and GGA. However, all these techniques currently assume a static environment, where demonstrative instances are procured beforehand, potentially unlimited time is provided to adequately search the parameter space, and the solver would never need to be retrained. This is not always the case in practice. The ReACT system, proposed in 2014, demonstrated that a solver could be configured during runtime as new instances arrive in a steady stream. This paper further develops that approach and shows how a ranking scheme, like TrueSkill, can further improve the configurator's performance, making it able to quickly find good parameterizations without adding any overhead on the time needed to solve any new instance, and then continuously improve as new instances are evaluated. The enhancements to ReACT that we present enable us to even outperform existing static configurators like SMAC in a non-dynamic setting.