Dynamic compartmental models for large ...
Document type :
Communication dans un congrès avec actes
Title :
Dynamic compartmental models for large multi-objective landscapes and performance estimation
Author(s) :
Monzón, Hugo [Auteur]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Faculty of Engineering [Nagano]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Scientific editor(s) :
Paquete, Luís
Zarges, Christine
Zarges, Christine
Conference title :
EvoCOP 2020 - 20th European Conference on Evolutionary Computation in Combinatorial Optimisation
City :
Seville
Country :
Espagne
Start date of the conference :
2020-04-15
Book title :
Evolutionary Computation in Combinatorial Optimization : 20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings
Journal title :
Lecture Notes in Computer Science
Publisher :
Springer
Publication date :
2020
English keyword(s) :
Compartmental models
Modeling
Multi-objective optimization
Population dynamics
Hypervolume estimation
Modeling
Multi-objective optimization
Population dynamics
Hypervolume estimation
HAL domain(s) :
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]
English abstract : [en]
Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems with 20 ...
Show more >Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems with 20 variables and used as a tool for algorithm analysis, algorithm comparison, and algorithm configuration assuming that the Pareto optimal set is known. In this paper, we introduce a new set of features based only on when non-dominated solutions are found in the population, relaxing the assumption that the Pareto optimal set is known in order to use Dynamic Compartment Models on larger problems. We also propose an auxiliary model to estimate the hypervolume from the features of population dynamics that measures the changes of new non-dominated solutions in the population. The new features are tested by studying the population changes on the Adaptive ϵ-Sampling ϵ-Hood while solving 30 instances of a 3 objective, 100 variables MNK-landscape problem. We also discuss the behavior of the auxiliary model and the quality of its hypervolume estimations.Show less >
Show more >Dynamic Compartmental Models are linear models inspired by epidemiology models to study Multi- and Many-Objective Evolutionary Algorithms dynamics. So far they have been tested on small MNK-Landscapes problems with 20 variables and used as a tool for algorithm analysis, algorithm comparison, and algorithm configuration assuming that the Pareto optimal set is known. In this paper, we introduce a new set of features based only on when non-dominated solutions are found in the population, relaxing the assumption that the Pareto optimal set is known in order to use Dynamic Compartment Models on larger problems. We also propose an auxiliary model to estimate the hypervolume from the features of population dynamics that measures the changes of new non-dominated solutions in the population. The new features are tested by studying the population changes on the Adaptive ϵ-Sampling ϵ-Hood while solving 30 instances of a 3 objective, 100 variables MNK-landscape problem. We also discuss the behavior of the auxiliary model and the quality of its hypervolume estimations.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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