Title :

Benchmarking the Pure Random Search on the Bi-objective BBOB-2016 Testbed

Author(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]

Conference title :

GECCO 2016 - Genetic and Evolutionary Computation Conference

City :

Denver, CO

Country :

Etats-Unis d'Amérique

Start date of the conference :

2016-07-20

Book title :

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

Publisher :

ACM

English keyword(s) :

Benchmarking
Black-box optimization
Bi-objective optimization

HAL domain(s) :

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

English abstract : [en]

The Comparing Continuous Optimizers platform COCO has become a standard for benchmarking numerical (single-objective) optimization algorithms effortlessly. In 2016, COCO has been extended towards multi-objective optimization ...
Show more >The Comparing Continuous Optimizers platform COCO has become a standard for benchmarking numerical (single-objective) optimization algorithms effortlessly. In 2016, COCO has been extended towards multi-objective optimization by providing a first bi-objective test suite. To provide a baseline, we benchmark a pure random search on this bi-objective bbob-biobj test suite of the COCO platform. For each combination of function, dimension n, and instance of the test suite, $10^6 · n$ candidate solutions are sampled uniformly within the sampling box $[−5, 5]^n$ .Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

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