A feature-based performance analysis in ...
Document type :
Communication dans un congrès avec actes
Title :
A feature-based performance analysis in evolutionary multiobjective optimization
Author(s) :
Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Daolio, Fabio [Auteur]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Daolio, Fabio [Auteur]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Conference title :
8th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2015)
City :
Guimarães
Country :
Portugal
Start date of the conference :
2015
Journal title :
Lecture Notes in Computer Science
Publication date :
2015
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Recherche d'information [cs.IR]
Informatique [cs]/Recherche d'information [cs.IR]
English abstract : [en]
This paper fundamentally investigates the performance of evolutionary multiobjective optimization (EMO) algorithms for computationally hard 0-1 combinatorial optimization, where a strict theoretical analysis is generally ...
Show more >This paper fundamentally investigates the performance of evolutionary multiobjective optimization (EMO) algorithms for computationally hard 0-1 combinatorial optimization, where a strict theoretical analysis is generally out of reach due to the high complexity of the underlying problem. Based on the examination of problem features from a multiobjective perspective, we improve the understanding of the efficiency of a simple dominance-based EMO algorithm with unbounded archive for multiobjective NK-landscapes with correlated objective values. More particularly, we adopt a statistical approach, based on simple and multiple linear regression analysis, to enquire the expected running time of global SEMO with restart for identifying a (1+ε)-approximation of the Pareto set for small-size enumerable instances. Our analysis provides further insights on the EMO search behavior and on the most important features that characterize the difficulty of an instance for this class of problems and algorithms.Show less >
Show more >This paper fundamentally investigates the performance of evolutionary multiobjective optimization (EMO) algorithms for computationally hard 0-1 combinatorial optimization, where a strict theoretical analysis is generally out of reach due to the high complexity of the underlying problem. Based on the examination of problem features from a multiobjective perspective, we improve the understanding of the efficiency of a simple dominance-based EMO algorithm with unbounded archive for multiobjective NK-landscapes with correlated objective values. More particularly, we adopt a statistical approach, based on simple and multiple linear regression analysis, to enquire the expected running time of global SEMO with restart for identifying a (1+ε)-approximation of the Pareto set for small-size enumerable instances. Our analysis provides further insights on the EMO search behavior and on the most important features that characterize the difficulty of an instance for this class of problems and algorithms.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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