TY - GEN
T1 - An experimental study of adaptive capping in irace
AU - Cáceres, Leslie Pérez
AU - López-Ibáñez, Manuel
AU - Hoos, Holger
AU - Stützle, Thomas
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - The irace package is a widely used for automatic algorithm configuration and implements various iterated racing procedures. The original irace was designed for the optimisation of the solution quality reached within a given running time, a situation frequently arising when configuring algorithms such as stochastic local search procedures. However, when applied to configuration scenarios that involve minimising the running time of a given target algorithm, irace falls short of reaching the performance of other general-purpose configuration approaches, since it tends to spend too much time evaluating poor configurations. In this article, we improve the efficacy of irace in running time minimisation by integrating an adaptive capping mechanism into irace, inspired by the one used by ParamILS. We demonstrate that the resulting iracecap reaches performance levels competitive with those of state-of-the-art algorithm configurators that have been designed to perform well on running time minimisation scenarios. We also investigate the behaviour of iracecap in detail and contrast different ways of integrating adaptive capping.
AB - The irace package is a widely used for automatic algorithm configuration and implements various iterated racing procedures. The original irace was designed for the optimisation of the solution quality reached within a given running time, a situation frequently arising when configuring algorithms such as stochastic local search procedures. However, when applied to configuration scenarios that involve minimising the running time of a given target algorithm, irace falls short of reaching the performance of other general-purpose configuration approaches, since it tends to spend too much time evaluating poor configurations. In this article, we improve the efficacy of irace in running time minimisation by integrating an adaptive capping mechanism into irace, inspired by the one used by ParamILS. We demonstrate that the resulting iracecap reaches performance levels competitive with those of state-of-the-art algorithm configurators that have been designed to perform well on running time minimisation scenarios. We also investigate the behaviour of iracecap in detail and contrast different ways of integrating adaptive capping.
UR - http://www.scopus.com/inward/record.url?scp=85034246933&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69404-7_17
DO - 10.1007/978-3-319-69404-7_17
M3 - Conference contribution
AN - SCOPUS:85034246933
SN - 9783319694030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 235
EP - 250
BT - Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers
A2 - Kvasov, Dmitri E.
A2 - Sergeyev, Yaroslav D.
A2 - Battiti, Roberto
A2 - Battiti, Roberto
A2 - Kvasov, Dmitri E.
A2 - Sergeyev, Yaroslav D.
PB - Springer Verlag
T2 - 11th International Conference on Learning and Intelligent Optimization, LION 2017
Y2 - 19 June 2017 through 21 June 2017
ER -