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Title: A statistical learning based approach for parameter fine-tuning of metaheuristics
Author: Calvet Liñan, Laura
Juan Pérez, Ángel Alejandro
Serrat Piè, Carles
Ries, Jana
Keywords: parameter fine-tuning
statistical learning
biased randomization
Issue Date: Jan-2016
Publisher: SORT: Statistics and Operations Research Transactions
Citation: Calvet Liñan, L., Juan, A.A., Serrat, C. & Ries, Jana (2016). "A statistical learning based approach for parameter fine-tuning of metaheuristics". SORT: Statistics and Operations Research Transactions, 40(1), pp. 1-24. ISSN 1696-2281.
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 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.
Language: English
ISSN: 1696-2281MIAR
Appears in Collections:Articles cientÍfics

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