Instance space analysis of combinatorial ...
Type de document :
Communication dans un congrès avec actes
Titre :
Instance space analysis of combinatorial multi-objective optimization problems
Auteur(s) :
Yap, Estefania [Auteur]
University of Melbourne
Muñoz, Mario [Auteur]
University of Melbourne
Smith-Miles, Kate [Auteur]
University of Melbourne
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Japanese French Laboratory for Informatics [JFLI]
University of Melbourne
Muñoz, Mario [Auteur]
University of Melbourne
Smith-Miles, Kate [Auteur]
University of Melbourne
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Japanese French Laboratory for Informatics [JFLI]
Titre de la manifestation scientifique :
IEEE CEC 2020 - Congress on Evolutionary Computation
Ville :
Glasgow
Pays :
Royaume-Uni
Date de début de la manifestation scientifique :
2020
Titre de l’ouvrage :
2020 IEEE Congress on Evolutionary Computation (CEC)
Éditeur :
IEEE
Mot(s)-clé(s) en anglais :
Multi-objective optimization
black-box combinatorial optimization
landscape analysis
feature-based performance prediction
black-box combinatorial optimization
landscape analysis
feature-based performance prediction
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Résumé en anglais : [en]
In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable ...
Lire la suite >In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space analysis on discrete multi-objective optimization problems (MOPs) for the first time under three different conditions. We create visualizations of the relationship between problem instances and algorithm performance for instance features previously identified using decision trees, as well an independent feature selection. The suitability of these features in discriminating between algorithm performance and understanding strengths and weaknesses is investigated. Furthermore, we explore the impact of various definitions of “good” performance. The visualization of the instance space provides an alternative method of algorithm discrimination by showing clusters of instances where algorithms perform well across the instance space. We validate the suitability of existing features and identify opportunities for future development.Lire moins >
Lire la suite >In recent years, there has been a continuous stream of development in evolutionary multi-objective optimization (EMO) algorithms. The large quantity of existing algorithms introduces difficulty in selecting suitable algorithms for a given problem instance. In this paper, we perform instance space analysis on discrete multi-objective optimization problems (MOPs) for the first time under three different conditions. We create visualizations of the relationship between problem instances and algorithm performance for instance features previously identified using decision trees, as well an independent feature selection. The suitability of these features in discriminating between algorithm performance and understanding strengths and weaknesses is investigated. Furthermore, we explore the impact of various definitions of “good” performance. The visualization of the instance space provides an alternative method of algorithm discrimination by showing clusters of instances where algorithms perform well across the instance space. We validate the suitability of existing features and identify opportunities for future development.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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