Distributed Adaptive Metaheuristic Selection: ...
Document type :
Communication dans un congrès avec actes
Title :
Distributed Adaptive Metaheuristic Selection: Comparisons of Selection Strategies
Author(s) :
Jankee, Christopher [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Fonlupt, Cyril [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Derbel, Bilel [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Fonlupt, Cyril [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Scientific editor(s) :
Bonnevay, Stéphane
Legrand, Pierrick
Monmarché, Nicolas
Lutton, Evelyne
Schoenauer, Marc
Legrand, Pierrick
Monmarché, Nicolas
Lutton, Evelyne
Schoenauer, Marc
Conference title :
12th International Conference on Artificial Evolution (EA 2015)
City :
Lyon
Country :
France
Start date of the conference :
2015-10-26
Book title :
Artificial Evolution12th International Conference, Evolution Artificielle, EA 2015, Lyon, France, October 26-28, 2015. Revised Selected Papers
Journal title :
Lecture Notes in Computer Science (LNCS, volume 9554)
Publisher :
Springer
Publication date :
2016-03-20
HAL domain(s) :
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [en]
In Distributed Adaptive Metaheuristics Selection (DAMS) methods, each computation node can select, at run-time during the optimizationprocess, one metaheuristic to be executed from a portfolio of available metaheuristics. ...
Show more >In Distributed Adaptive Metaheuristics Selection (DAMS) methods, each computation node can select, at run-time during the optimizationprocess, one metaheuristic to be executed from a portfolio of available metaheuristics. Within the DAMS framework, we investigate different metaheuristic selection strategies which enable to choose locally at each time step a metaheuristic to execute. We conduct a throughout experimental analysis in order to better understand the accuracy and the behavior of the proposed strategies, as well as their relative performance. In particular, we analyze the impact of sharing metaheuristic performance information between compute nodes and the relative effect on each of the considered distributed selection strategies depending on communication topology. Our experimental analysis is performed on the simple one Max problem, for which the best metaheuristics that should be executed at run-time are known, as well as on the more sophisticated NK-landscapes for which non-linearity can be tuned.Show less >
Show more >In Distributed Adaptive Metaheuristics Selection (DAMS) methods, each computation node can select, at run-time during the optimizationprocess, one metaheuristic to be executed from a portfolio of available metaheuristics. Within the DAMS framework, we investigate different metaheuristic selection strategies which enable to choose locally at each time step a metaheuristic to execute. We conduct a throughout experimental analysis in order to better understand the accuracy and the behavior of the proposed strategies, as well as their relative performance. In particular, we analyze the impact of sharing metaheuristic performance information between compute nodes and the relative effect on each of the considered distributed selection strategies depending on communication topology. Our experimental analysis is performed on the simple one Max problem, for which the best metaheuristics that should be executed at run-time are known, as well as on the more sophisticated NK-landscapes for which non-linearity can be tuned.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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