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Statistical learning techniques applied to parameter fine-tunig in metaheuristics

Author
Serrat, C.; Calvet , L.; Juan, Á.; Ries, J.
Type of activity
Presentation of work at congresses
Name of edition
CLAPEM 2016 - XIV Latin American Congress of Probability and Mathematical Statistics
Date of publication
2016
Presentation's date
2016-12
Book of congress proceedings
CLAPEM 2016 - XIV Latin American Congress of Probability and Mathematical Statistics [Abstract book]
First page
78
Last page
78
Abstract
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 aim of the talk is to focus on the statistical procedures used so far by the scientific community to tackle the Parameter Setting Problem, and to propose a novel and more efficient methodology. The proposal is tested for the selection of appropriate parameter values for solving the Multi-Depot Vehicle Routing Problem.
Keywords
Parameter fine-tuning, biased randomization, metaheuristics, statistical learning
Group of research
GRBIO - Biostatistics and Bioinformatics Research Group

Participants

  • Serrat Pie, Carles  (author and speaker )
  • Calvet Liñán, Laura  (author and speaker )
  • Juan Pérez, Ángel Alejandro  (author and speaker )
  • Ries, Jana  (author and speaker )