Resumen
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 valuescan lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategiesare step by step methods based on multiple runs of the metaheuristic algorithm. In this study we comparefour automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of eachmethod using a standard genetic algorithm for continuous function optimization. We discuss about therequirements 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.
| Idioma original | English |
|---|---|
| Páginas (desde-hasta) | 39-51 |
| Número de páginas | 13 |
| Publicación | Applied Soft Computing Journal |
| Volumen | 17 |
| DOI | |
| Estado | Published - 1 ene 2014 |
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ASJC Scopus subject areas
- Software
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A beginner's guide to tuning methods. / Montero, Elizabeth; Riff, María Cristina; Neveu, Bertrand.
En: Applied Soft Computing Journal, Vol. 17, 01.01.2014, p. 39-51.Resultado de la investigación: Article
TY - JOUR
T1 - A beginner's guide to tuning methods
AU - Montero, Elizabeth
AU - Riff, María Cristina
AU - Neveu, Bertrand
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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 valuescan lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategiesare step by step methods based on multiple runs of the metaheuristic algorithm. In this study we comparefour automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of eachmethod using a standard genetic algorithm for continuous function optimization. We discuss about therequirements 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.
AB - 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 valuescan lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategiesare step by step methods based on multiple runs of the metaheuristic algorithm. In this study we comparefour automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of eachmethod using a standard genetic algorithm for continuous function optimization. We discuss about therequirements 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.
KW - Evolutionary algorithms
KW - Metaheuristics
KW - Parameter setting problem
KW - Tuning methods
UR - http://www.scopus.com/inward/record.url?scp=84893109228&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2013.12.017
DO - 10.1016/j.asoc.2013.12.017
M3 - Article
VL - 17
SP - 39
EP - 51
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
ER -