A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation

Diego Tapia, BRODERICK CRAWFORD LABRIN, RICARDO JAVIER SOTO DE GIORGIS, Felipe Cisternas-Caneo, José Lemus-Romani, Mauricio Castillo, José García, WENCESLAO ENRIQUE PALMA MUÑOZ, Fernando Paredes, Sanjay Misra

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

7 Citas (Scopus)

Resumen

Many Metaheuristics solve optimization problems in the continuous domain, so it is necessary to apply binarization schemes to solve binary problems, this selection that is not trivial since it impacts the heart of the search strategy: its ability to explore. This paper proposes a Hyperheuristic Binarization Framework based on a Machine Learning technique of Reinforcement Learning to select the appropriate binarization strategy, which is applied in a Low Level Metaheuristic. The proposed implementation is composed of a High Level Metaheuristic, Ant Colony Optimization, using Q-Learning replacing the pheromone trace component. In the Low Level Metaheuristic, we use a Grey Wolf Optimizer to solve the binary problem with binarization scheme fixed by ants. This framework allowing a better balance between exploration and exploitation, and can be applied selecting others low level components.

Idioma originalInglés
Título de la publicación alojadaApplied Informatics - 3rd International Conference, ICAI 2020, Proceedings
EditoresHector Florez, Sanjay Misra
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas14-28
Número de páginas15
ISBN (versión impresa)9783030617011
DOI
EstadoPublicada - 2020
Publicado de forma externa
Evento3rd International Conference on Applied Informatics, ICAI 2020 - Ota, Nigeria
Duración: 29 oct. 202031 oct. 2020

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1277 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

Conferencia3rd International Conference on Applied Informatics, ICAI 2020
País/TerritorioNigeria
CiudadOta
Período29/10/2031/10/20

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