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

Laura Calvet Mir , Ángel Alejandro Juan Pérez , Carles Serrat i Piè , Jana Ries

• 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