Q-learnheuristics: Towards data-driven balanced metaheuristics

Broderick Crawford, Ricardo Soto, José Lemus-Romani, Marcelo Becerra-Rozas, José M. Lanza-Gutiérrez, Nuria Caballé, Mauricio Castillo, Diego Tapia, Felipe Cisternas-Caneo, José García, Gino Astorga, Carlos Castro, José Miguel Rubio

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the explorationexploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

Original languageEnglish
Article number1839
Issue number16
StatePublished - 2 Aug 2021


  • Balanced metaheuristics
  • Metaheuristics
  • Q-Learning
  • Sine-Cosine Algorithm
  • Whale Optimization Algorithm


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