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

Laura Calvet ^[3] ; Angel A. Juan ^[3] ; Carles Serrat ^[2] ; Jana Ries ^[1]
1. [1] University of Portsmouth
  
  University of Portsmouth
  
  Southsea, Reino Unido
2. [2] Universitat Politècnica de Catalunya
  
  Universitat Politècnica de Catalunya
  
  Barcelona, España
3. [3] UOC
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Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 40, Nº. 1, 2016, págs. 201-224
Idioma: inglés
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Resumen
- Metaheuristics are approximation methods used to solve com binatorial optimization problems.
  
  Their performance usually depends on a set of parameters tha t need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provide s an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the sta tistical procedures employed so far by the scientific community. In addition, a novel methodolog y is proposed, which is tested using an already existing algorithm for solving the Multi-Depot V ehicle Routing Problem