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

Blot, Aymeric; Hoos, Holger; Jourdan, Laetitia; Kessaci, Marie-Eleonore; Trautmann, Heike

Type de document :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-319-50349-3_3

Titre :

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

Auteur(s) :

Blot, Aymeric [Auteur correspondant]
Université de Lille
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
École normale supérieure - Rennes [ENS Rennes]
University of British Columbia [Canada] [UBC]
Hoos, Holger [Auteur]
University of British Columbia [Canada] [UBC]
Jourdan, Laetitia [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Université de Lille
Kessaci, Marie-Eleonore [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Université de Lille
Trautmann, Heike [Auteur]
Westfälische Wilhelms-Universität Münster = University of Münster [WWU]

Titre de la manifestation scientifique :

Learning and Intelligent Optimization

Ville :

Ischia

Pays :

Italie

Date de début de la manifestation scientifique :

2016-05-29

Titre de l’ouvrage :

Learning and Intelligent Optimization

Titre de la revue :

Lecture Notes in Computer Science

Mot(s)-clé(s) en anglais :

Local Search Algorithms
Algorithm Configuration
Multi-objective Optimisation
Parameter Tuning

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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