Title :

Dynamic compartmental models for algorithm analysis and population size estimation

Author(s) :

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

Conference title :

GECCO 2019 - Genetic and Evolutionary Computation Conference

City :

Prague

Country :

République tchèque

Start date of the conference :

2019-07-13

Journal title :

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

Publisher :

ACM Press

Publication date :

2019-07

English keyword(s) :

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

HAL domain(s) :

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

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL