Name
Abstract | ||
|---|---|---|
Parameter setting problem has demonstrated being a relevant problem related to the use of metaheuristics. ParamILS and I-Race are sophisticated tuning methods that can provide valuable information for designers as well as manage conditional parameters. However, the quality of parameter configurations they can find strongly depends on a proper definition of parameter search space. Evoca is a recently proposed tuner which has demonstrated being less sensitive to the setup of parameters search space. In this paper, we propose an effective collaborative approach that combines Evoca and I-Race as well as Evoca and ParamILS. In both collaborative strategies, Evoca is used to define a proper parameter search space for each tuner. Results demonstrated that the collaborative approaches studied are able to find good parameter configurations reducing the effort required to properly define the parameter search space. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00500-019-04252-4 | Soft Computing |
Keywords | Field | DocType |
Tuning methods, ParamILS, I-Race, Evoca, Collaborative approach, Parameter search space | Computer science,Artificial intelligence,Computer engineering,Machine learning,Tuner,Metaheuristic | Journal |
Volume | Issue | ISSN |
24 | 7 | 1432-7643 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Montero | 1 | 69 | 10.14 |
| María Cristina Riff | 2 | 17 | 6.72 |