On the Design of a Master-Worker Adaptive Algorithm Selection Framework

Jankee, Christopher; Verel, Sébastien; Derbel, Bilel; Fonlupt, Cyril

Type de document :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-319-78133-4_1

Titre :

On the Design of a Master-Worker Adaptive Algorithm Selection Framework

Auteur(s) :

Jankee, Christopher [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Derbel, Bilel [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Fonlupt, Cyril [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]

Titre de la manifestation scientifique :

EA 2017 - 13th International Conference on Artificial Evolution

Ville :

Paris

Pays :

France

Date de début de la manifestation scientifique :

2017-10-25

Titre de l’ouvrage :

EA 2017: Artificial Evolution

Titre de la revue :

LNCS

Éditeur :

Springer

Date de publication :

2018-03-20

Discipline(s) HAL :

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

Résumé en anglais : [en]

We investigate the design of a master-worker schemes for adaptive algorithm selection with the following two-fold goal: (i) choose accurately from a given portfolio a set of operators to be executed in parallel, and ...
Lire la suite >We investigate the design of a master-worker schemes for adaptive algorithm selection with the following two-fold goal: (i) choose accurately from a given portfolio a set of operators to be executed in parallel, and consequently (ii) take full advantage of the compute power offered by the underlying distributed environment. In fact, it is still an open issue to design online distributed strategies that are able to optimally assign operators to parallel compute resources when distributively solving a given optimization problem. In our proposed framework, we adopt a reward-based perspective and investigate at what extent the average or maximum rewards collected at the master from the workers are appropriate. Moreover, we investigate the design of both homogeneous and heterogeneous scheme. Our comprehensive experimental study, conducted through a simulation-based methodology and using a recently proposed benchmark family for adaptive algorithm selection, reveals the accuracy of the proposed framework while providing new insights on the performance of distributed adaptive optimization algorithms.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :