Name
Abstract | ||
|---|---|---|
ParamILS, I-Race and Evoca are well-known tuning methods designed to search quality parameter calibrations for metaheuristic algorithms. The set-up of parameter search space can strongly affect the performance of tuning methods. In this work we study how the parameter search definitions affect the quality of parameter calibrations delivered by these tuners. An experimental evaluation using two well known metaheuristic algorithms and a real life case is presented. We also provide some guidelines to consider when defining parameters search spaces according to the tuner used in order to obtain the best performance they can find. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.engappai.2018.09.001 | Engineering Applications of Artificial Intelligence |
Keywords | Field | DocType |
Parameter setting,Tuning methods,Parameter search space definition | Mathematical optimization,Computer science,Calibration,Tuner,Metaheuristic | Journal |
Volume | ISSN | Citations |
76 | 0952-1976 | 0 |
PageRank | References | Authors |
0.34 | 20 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Montero | 1 | 69 | 10.14 |
| María Cristina Riff | 2 | 200 | 23.91 |
| Nicolás Rojas-Morales | 3 | 5 | 2.41 |