An ensemble indicator-based density estimator ...
Document type :
Communication dans un congrès avec actes
Title :
An ensemble indicator-based density estimator for evolutionary multi-objective optimization
Author(s) :
Falcón-Cardona, Jesús Guillermo [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Liefooghe, Arnaud [Auteur]
Japanese French Laboratory for Informatics [JFLI]
Optimisation de grande taille et calcul large échelle [BONUS]
Coello Coello, Carlos [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Liefooghe, Arnaud [Auteur]
Japanese French Laboratory for Informatics [JFLI]
Optimisation de grande taille et calcul large échelle [BONUS]
Coello Coello, Carlos [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Conference title :
PPSN 2020 - Sixteenth International Conference on Parallel Problem Solving from Nature
City :
Leiden
Country :
Pays-Bas
Start date of the conference :
2020-09-05
Book title :
Lecture Notes in Computer Science
Journal title :
Lecture Notes in Computer Science
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [en]
Ensemble learning is one of the most employed methods in machine learning. Its main ground is the construction of stronger mechanisms based on the combination of elementary ones. In this paper, we employ AdaBoost, which ...
Show more >Ensemble learning is one of the most employed methods in machine learning. Its main ground is the construction of stronger mechanisms based on the combination of elementary ones. In this paper, we employ AdaBoost, which is one of the most well-known ensemble methods, to generate an ensemble indicator-based density estimator for multi-objective optimization. It combines the search properties of five density estimators, based on the hypervolume, R2, IGD+, ε+, and ∆p quality indicators. Through the multi-objective evolutionary search process, the proposed ensemble mechanism adapts itself using a learning process that takes the preferences of the underlying quality indicators into account. The proposed method gives rise to the ensemble indicator-based multi-objective evolutionary algorithm (EIB-MOEA) that shows a robust performance on different multi-objective optimization problems when compared with respect to several existing indicator-based multi-objective evolutionary algorithms.Show less >
Show more >Ensemble learning is one of the most employed methods in machine learning. Its main ground is the construction of stronger mechanisms based on the combination of elementary ones. In this paper, we employ AdaBoost, which is one of the most well-known ensemble methods, to generate an ensemble indicator-based density estimator for multi-objective optimization. It combines the search properties of five density estimators, based on the hypervolume, R2, IGD+, ε+, and ∆p quality indicators. Through the multi-objective evolutionary search process, the proposed ensemble mechanism adapts itself using a learning process that takes the preferences of the underlying quality indicators into account. The proposed method gives rise to the ensemble indicator-based multi-objective evolutionary algorithm (EIB-MOEA) that shows a robust performance on different multi-objective optimization problems when compared with respect to several existing indicator-based multi-objective evolutionary algorithms.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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