A statistical learning based approach for parameter fine-tuning of metaheuristics

Laura Calvet, Angel A. Juan, Carles Serrat, Jana Ries

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Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.
Original languageEnglish
Pages (from-to)201-224
Number of pages24
JournalSORT - Statistics and Operations Research Transactions
Issue number1
Early online date17 Jun 2016
Publication statusPublished - 30 Jun 2016


  • Parameter fine-tuning
  • metaheuristics
  • statistical learning
  • biased randomization


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