Titre :

The Impact of Search Volume on the Performance of RANDOMSEARCH on the Bi-objective BBOB-2016 Test Suite

Auteur(s) :

Auger, Anne [Auteur]
Machine Learning and Optimisation [TAO]
Brockhoff, Dimo [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Hansen, Nikolaus [Auteur]
Machine Learning and Optimisation [TAO]
Tušar, Dejan [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Tušar, Tea [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Wagner, Tobias [Auteur]
Technische Universität Dortmund [Dortmund] [TU]

Titre de la manifestation scientifique :

GECCO 2016 - Genetic and Evolutionary Computation Conference

Ville :

Denver, CO

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2016-07-20

Titre de l’ouvrage :

GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Éditeur :

ACM

Date de publication :

2016

Mot(s)-clé(s) en anglais :

Bi-objective optimization
Benchmarking
Black-box optimization

Discipline(s) HAL :

Informatique [cs]/Réseau de neurones [cs.NE]
Mathématiques [math]/Optimisation et contrôle [math.OC]

Résumé en anglais : [en]

Pure random search is undeniably the simplest stochastic search algorithm for numerical optimization. Essentially the only thing to be determined to implement the algorithm is its sampling space, the influence of which on ...
Lire la suite >Pure random search is undeniably the simplest stochastic search algorithm for numerical optimization. Essentially the only thing to be determined to implement the algorithm is its sampling space, the influence of which on the performance on the bi-objective bbob-biobj test suite of the COCO platform is investigated here. It turns out that the suggested region of interest of [−100, 100] n (with n being the problem dimension) has a too vast volume for the algorithm to approximate the Pareto set effectively. Better performance can be achieved if solutions are sampled uniformly within [−5, 5] n or [−4, 4] n. The latter sampling box corresponds to the smallest hypercube encapsulating all single-objective optima of the 55 constructed bi-objective problems of the bbob-biobj test suite. However, not all best known Pareto set approximations are entirely contained within [−5, 5] n .Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Analyse, Amelioration, Evaluation d'algorithmes numériques pour l'optimisation boîte-noire

Collections :

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

Source :

Harvested from HAL