Treffer: MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework

Title:
MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework
Contributors:
Université de Lille, Parallel Cooperative Multi-criteria Optimization (DOLPHIN), Centre Inria de l'Université de Lille, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Rennes (ENS Rennes), University of British Columbia [Canada] (UBC), Westfälische Wilhelms-Universität Münster = University of Münster = Université de Münster [Münster, Allemagne] (WWU)
Source:
Learning and Intelligent Optimization. :32-47
Publisher Information:
CCSD, 2016.
Publication Year:
2016
Collection:
collection:CNRS
collection:INRIA
collection:INRIA-LILLE
collection:INRIA_TEST
collection:TESTALAIN1
collection:CRISTAL
collection:INRIA2
collection:TDS-MACS
collection:UNIV-RENNES
collection:UNIV-LILLE
collection:INRIA-CANADA
collection:INRIA-ALLEMAGNE
Subject Geographic:
Original Identifier:
HAL: hal-01370392
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-50349-3_3
DOI:
10.1007/978-3-319-50349-3_3
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by-nc/
Accession Number:
edshal.hal.01370392v1
Database:
HAL

Weitere Informationen

Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.