Title :

The Impact of Variation Operators on the Performance of SMS-EMOA on the Bi-objective BBOB-2016 Test Suite

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

Publication date :

2016

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 S-metric-Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) is one of the best-known indicator-based multi-objective optimization algorithms. It employs the S-metric or hypervolume indicator in ...
Show more >The S-metric-Selection Evolutionary Multi-objective Optimization Algorithm (SMS-EMOA) is one of the best-known indicator-based multi-objective optimization algorithms. It employs the S-metric or hypervolume indicator in its (steady-state) selection by deleting in each iteration the solution that has the smallest contribution to the hypervolume indicator. In the SMS-EMOA, the conceptual idea is this hypervolume-based selection. Hence the algorithm can, for example, be combined with several variation operators. Here, we benchmark two versions of SMS-EMOA which employ differential evolution (DE) and simulated binary crossover (SBX) with polynomial mutation (PM) respectively on the newly introduced bi-objective bbob-biobj test suite of the Comparing Continuous Optimizers (COCO) platform. The results un-surprisingly reveal that the choice of the variation operator is crucial for performance with a clear advantage of the DE variant on almost all functions.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