Studying MOEAs Dynamics and their Performance using a Three Compartmental Model

Monzón, Hugo; Aguirre, Hernan; Verel, Sébastien; Liefooghe, Arnaud; Derbel, Bilel; Tanaka, Kiyoshi

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...)

Title :

Studying MOEAs Dynamics and their Performance using a Three Compartmental Model

Author(s) :

Monzón, Hugo [Auteur]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Liefooghe, Arnaud [Auteur]

Optimisation de grande taille et calcul large échelle [BONUS]
Derbel, Bilel [Auteur]

Optimisation de grande taille et calcul large échelle [BONUS]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]

Conference title :

GECCO 2018 - Genetic and Evolutionary Computation Conference Companion

City :

Kyoto

Country :

Japon

Start date of the conference :

2018-07-15

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

The road to a better design of multi- and many-objective evolutionary algorithms requires a deeper understanding of their behavior. A step on this road has recently been taken with the proposal of compartmental models to ...
Show more >The road to a better design of multi- and many-objective evolutionary algorithms requires a deeper understanding of their behavior. A step on this road has recently been taken with the proposal of compartmental models to study population dynamics. In this work, we push this step further by introducing a new set of features that we link with algorithm performance. By tracking the number of newly discovered Pareto Optimal (PO) solutions, the previously-found PO solutions and the remaining non-PO solutions, we can track the algorithm progression. By relating these features with a performance measure, such as the hypervolume, we can analyze their relevance for algorithm comparison. This study considers out-of-the-box implementations of recognized multi- and many-objective optimizers belonging to popular classes such as conventional Pareto dominance, extensions of dominance, indicator, and decomposition based approaches. In order to generate training data for the compartmental models, we consider multiple instances of MNK-landscapes with different numbers of objectives.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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