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

Identifier:  RP:2445

Handle: https://hdl.handle.net/20.500.11797/RP2445

Authors:  Ries, Jana; Serrat, Carles; Juan, Angel A.; Calvet, Laura

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.