An ensemble indicator-based density estimator ...
Type de document :
Communication dans un congrès avec actes
Titre :
An ensemble indicator-based density estimator for evolutionary multi-objective optimization
Auteur(s) :
Falcón-Cardona, Jesús Guillermo [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Japanese French Laboratory for Informatics [JFLI]
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]
Optimisation de grande taille et calcul large échelle [BONUS]
Japanese French Laboratory for Informatics [JFLI]
Coello Coello, Carlos [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Titre de la manifestation scientifique :
PPSN 2020 - Sixteenth International Conference on Parallel Problem Solving from Nature
Ville :
Leiden
Pays :
Pays-Bas
Date de début de la manifestation scientifique :
2020-09-05
Titre de l’ouvrage :
Lecture Notes in Computer Science
Titre de la revue :
Lecture Notes in Computer Science
Discipline(s) HAL :
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]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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