Approaches for many-objective optimization: ...
Document type :
Communication dans un congrès avec actes
Title :
Approaches for many-objective optimization: analysis and comparison on MNK-landscapes
Author(s) :
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Zapotecas-Martínez, Saúl [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Faculty of Engineering [Nagano]
Zapotecas-Martínez, Saúl [Auteur]
Faculty of Engineering [Nagano]
Liefooghe, Arnaud [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Scientific editor(s) :
Bonnevay, Stéphane
Legrand, Pierrick
Monmarché, Nicolas
Lutton, Evelyne
Schoenauer, Marc
Legrand, Pierrick
Monmarché, Nicolas
Lutton, Evelyne
Schoenauer, Marc
Conference title :
13th International Conference on Artificial Evolution (EA 2015)
City :
Lyon
Country :
France
Start date of the conference :
2015-10-26
Book title :
Artificial Evolution12th International Conference, Evolution Artificielle, EA 2015, Lyon, France, October 26-28, 2015. Revised Selected Papers
Journal title :
Lecture Notes in Computer Science (LNCS)
Publisher :
Springer
Publication date :
2016-03-20
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [en]
This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive ε and the hypervolume difference indicators, and AεSεH as representative algorithms ofdecomposition, indicators, and ...
Show more >This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive ε and the hypervolume difference indicators, and AεSεH as representative algorithms ofdecomposition, indicators, and ε-dominance based approaches for manyobjective optimization. We use small MNK-landscapes to trace the dynamics of the algorithms generating high-resolution approximations ofthe Pareto optimal set. Also, we use large MNK-landscapes to analyzetheir scalability to larger search spaces.Show less >
Show more >This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive ε and the hypervolume difference indicators, and AεSεH as representative algorithms ofdecomposition, indicators, and ε-dominance based approaches for manyobjective optimization. We use small MNK-landscapes to trace the dynamics of the algorithms generating high-resolution approximations ofthe Pareto optimal set. Also, we use large MNK-landscapes to analyzetheir scalability to larger search spaces.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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