Name
Abstract | ||
|---|---|---|
Most metaheuristics define a set of parameters that must be tuned. A good setup of those parameter values can lead to take advantage of all the metaheuristic capabilities to solve the problem at hand. Tuning techniques are step by step methods based on multiple runs of the algorithm. In this study we compare three automated tuning methods: F-Race, Revac and ParamILS. We evaluate the performance of each method using a genetic algorithm for combinatorial optimization. The differences and advantages of each technique are discussed. Finally we establish some guidelines that might help to choose a tuning process to use. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1830483.1830540 | GECCO |
Keywords | Field | DocType |
parameter value,good setup,automated tuning method,genetic algorithm,metaheuristic capability,evolutionary algorithm,tuning process,multiple run,combinatorial optimization,step method,tuning technique,off-line calibration technique,evolutionary algorithms | Mathematical optimization,Off line,Evolutionary algorithm,Parallel metaheuristic,Computer science,Algorithm,Combinatorial optimization,Artificial intelligence,Genetic algorithm,Calibration,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
2 | 0.37 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Montero | 1 | 69 | 10.14 |
| María Cristina Riff | 2 | 200 | 23.91 |
| Bertrand Neveu | 3 | 253 | 23.18 |