A beginner's guide to tuning methods

Montero, E; Riff M.C.; Neveu, B

Abstract

Metaheuristic methods have been demonstrated to be efficient tools to solve hard optimization problems. Most metaheuristics define a set of parameters that must be tuned. A good setup of that parameter values can lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategies are step by step methods based on multiple runs of the metaheuristic algorithm. In this study we compare four automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of each method using a standard genetic algorithm for continuous function optimization. We discuss about the requirements of each method, the resources used and quality of solutions found in different scenarios. Finally we establish some guidelines that can help to choose the more appropriate tuning procedure. (C) 2013 Elsevier B. V. All rights reserved.

Más información

Título según WOS: A beginner's guide to tuning methods
Título según SCOPUS: A beginner's guide to tuning methods
Título de la Revista: APPLIED SOFT COMPUTING
Volumen: 17
Editorial: ELSEVIER SCIENCE BV
Fecha de publicación: 2014
Página de inicio: 39
Página final: 51
Idioma: English
URL: http://linkinghub.elsevier.com/retrieve/pii/S1568494613004468
DOI:

10.1016/j.asoc.2013.12.017

Notas: ISI, SCOPUS