@inproceedings{06fd568e29e443d8ae6c4cc19546618f,

title = "A unified race algorithm for offline parameter tuning",

abstract = "This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods.",

author = "{van Dijk}, Tim and Martijn Mes and Marco Schutten and Joaquim Gromicho",

year = "2014",

doi = "10.1109/WSC.2014.7020222",

language = "English",

isbn = "978-1-4799-7484-9",

series = "Proceedings of the Winter Simulation Conference",

publisher = "IEEE",

pages = "3971--3982",

booktitle = "Proceedings of the 2014 Winter Simulation Conference (WSC '14)",

address = "United States",

}