Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2016

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

  • Datos identificativos

    Identificador: RP:2445
    Handle: https://hdl.handle.net/20.500.11797/RP2445
    Autores:
    Ries, JanaSerrat, CarlesJuan, Angel A.Calvet, Laura
    Resumen:
    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.

  • Otros:

    Autor/es de la URV: Ries, Jana Serrat, Carles Juan, Angel A. Calvet, Laura
    Palabras clave: Parameter fine-tuning, metaheuristics, statistical learning, biased randomization
    Resumen: 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.
    Año de publicación de la revista: 2016
    Tipo de publicación: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Palabras clave:

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

  • Documentos:

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