Titre :

Dynamic compartmental models for algorithm analysis and population size estimation

Auteur(s) :

Monzón, Hugo [Auteur]
Shinshu University [Nagano]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Shinshu University [Nagano]
Faculty of Engineering [Nagano]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Liefooghe, Arnaud [Auteur]
Université de Lille, Sciences et Technologies
Optimisation de grande taille et calcul large échelle [BONUS]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Derbel, Bilel [Auteur]
Université de Lille, Sciences et Technologies
Optimisation de grande taille et calcul large échelle [BONUS]
Tanaka, Kiyoshi [Auteur]
Shinshu University [Nagano]
Faculty of Engineering [Nagano]

Titre de la manifestation scientifique :

GECCO 2019 - Genetic and Evolutionary Computation Conference

Ville :

Prague

Pays :

République tchèque

Date de début de la manifestation scientifique :

2019-07-13

Titre de la revue :

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference

Éditeur :

ACM Press

Date de publication :

2019-07

Mot(s)-clé(s) en anglais :

Empirical study
Working principles of evolutionary computing
Genetic algorithms
Multi-objective optimization
Compartmental Models
Modeling

Discipline(s) HAL :

Mathématiques [math]/Optimisation et contrôle [math.OC]

Résumé en anglais : [en]

Dynamic Compartmental Models (DCM) can be used to study the population dynamics of Multi- and Many-objective Optimization Evolutionary Algorithms (MOEAs). These models track the composition of the instantaneous population ...
Lire la suite >Dynamic Compartmental Models (DCM) can be used to study the population dynamics of Multi- and Many-objective Optimization Evolutionary Algorithms (MOEAs). These models track the composition of the instantaneous population by grouping them in compartments and capture their behavior in a set of values, creating a compact representation for analysis and comparison of algorithms. Furthermore, the use of DCMs is not limited to analysis, by creating models of the same algorithm with different configurations is possible to extract new models by interpolation, and use them to explore fine-grained configurations lying between the ones used as a base. We illustrate the use of the model on some Multi- and Many-objective algorithms, run on enumerable MNK-Landscapes instances with 6 objectives for the analysis, and 5 objectives when used as a tool to do configuration.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL