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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationApplied Informatics - 3rd International Conference, ICAI 2020, Proceedings
EditorsHector Florez, Sanjay Misra
PublisherSpringer Science and Business Media Deutschland GmbH
Pages14-28
Number of pages15
ISBN (Print)9783030617011
DOIs
StatePublished - 2020
Externally publishedYes
Event3rd International Conference on Applied Informatics, ICAI 2020 - Ota, Nigeria
Duration: 29 Oct 202031 Oct 2020

Publication series

NameCommunications in Computer and Information Science
Volume1277 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Applied Informatics, ICAI 2020
Country/TerritoryNigeria
CityOta
Period29/10/2031/10/20

Keywords

  • Binarization framework
  • Combinatorial optimization
  • Hyperheuristics
  • Metaheuristics
  • Reinforcement learning

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