Shake them all! Rethinking Selection and ...
Document type :
Communication dans un congrès avec actes
Title :
Shake them all! Rethinking Selection and Replacement in MOEA/D
Author(s) :
Marquet, Gauvain [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Liefooghe, Arnaud [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Liefooghe, Arnaud [Auteur]

Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Conference title :
Parallel Problem Solving from Nature (PPSN)
City :
Ljubljana
Country :
Slovénie
Start date of the conference :
2014-09-11
Publisher :
LNCS
Publication date :
2014-09-16
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Algorithme et structure de données [cs.DS]
Informatique [cs]/Algorithme et structure de données [cs.DS]
English abstract : [en]
We build upon the previous efforts to enhance the search ability of Moead (a decomposition-based algorithm), by investigating the idea of evolving the whole population simultaneously at once. We thereby propose new alternative ...
Show more >We build upon the previous efforts to enhance the search ability of Moead (a decomposition-based algorithm), by investigating the idea of evolving the whole population simultaneously at once. We thereby propose new alternative selection and replacement strategies that can be combined in different ways within a generic and problem-independent framework. To assess the performance of our strategies, we conduct a comprehensive experimental study on bi-objective combinatorial optimization problems. More precisely, we consider ρMNK-landscapes and knapsack problems as a benchmark, and experiment a wide range of parameter configurations for Moead and its variants. Our analysis reveals the effectiveness of our strategies and their robustness to parameter settings. In particular, substantial improvements are obtained compared to the conventional Moead.Show less >
Show more >We build upon the previous efforts to enhance the search ability of Moead (a decomposition-based algorithm), by investigating the idea of evolving the whole population simultaneously at once. We thereby propose new alternative selection and replacement strategies that can be combined in different ways within a generic and problem-independent framework. To assess the performance of our strategies, we conduct a comprehensive experimental study on bi-objective combinatorial optimization problems. More precisely, we consider ρMNK-landscapes and knapsack problems as a benchmark, and experiment a wide range of parameter configurations for Moead and its variants. Our analysis reveals the effectiveness of our strategies and their robustness to parameter settings. In particular, substantial improvements are obtained compared to the conventional Moead.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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