TY - GEN
T1 - A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation
AU - Tapia, Diego
AU - CRAWFORD LABRIN, BRODERICK
AU - SOTO DE GIORGIS, RICARDO JAVIER
AU - Cisternas-Caneo, Felipe
AU - Lemus-Romani, José
AU - Castillo, Mauricio
AU - García, José
AU - PALMA MUÑOZ, WENCESLAO ENRIQUE
AU - Paredes, Fernando
AU - Misra, Sanjay
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Binarization framework
KW - Combinatorial optimization
KW - Hyperheuristics
KW - Metaheuristics
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85096411243&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61702-8_2
DO - 10.1007/978-3-030-61702-8_2
M3 - Conference contribution
AN - SCOPUS:85096411243
SN - 9783030617011
T3 - Communications in Computer and Information Science
SP - 14
EP - 28
BT - Applied Informatics - 3rd International Conference, ICAI 2020, Proceedings
A2 - Florez, Hector
A2 - Misra, Sanjay
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 29 October 2020 through 31 October 2020
ER -