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Studying MOEAs Dynamics and their Performance ...
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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] refId
Optimisation de grande taille et calcul large échelle [BONUS]
Derbel, Bilel [Auteur] refId
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 ...
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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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