Warmstarting of Model-Based Algorithm Configuration

verfasst von

Marius Lindauer, Frank Hutter

Abstract

The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.

Externe Organisation(en)

Albert-Ludwigs-Universität Freiburg

Typ

Aufsatz in Konferenzband

Seiten

1355-1362

Anzahl der Seiten

8

Publikationsdatum

2018

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence

Elektronische Version(en)

https://arxiv.org/abs/1709.04636v3 (Zugang: Offen)
https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17235/15829 (Zugang: Geschlossen)