Name
Abstract | ||
|---|---|---|
The process of designing a metaheuristic is a difficult and time consuming task as it usually requires tuning to find the best associated parameter values. In this paper, we propose a simple tuning tool called EVOCA which allows unexperimented metaheuristic designers to obtain good quality results without have a strong knowledge in tuning methods. The simplicity here means that the designer does not have to care about the initial settings of the tuner. We apply EVOCA to a genetic algorithm that solves NK landscape instances of various categories. We show that EVOCA is able to tune both categorical and numerical parameters allowing the designer to discard ineffective components for the algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CEC.2013.6557972 | 2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) |
Keywords | Field | DocType |
calibration,genetic algorithms,statistics,algorithm design and analysis,genetic algorithm,sociology | Mathematical optimization,Computer science,Categorical variable,Algorithm,Artificial intelligence,Genetic algorithm,Tuner,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
10 | 0.71 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Cristina Riff | 1 | 200 | 23.91 |
| Elizabeth Montero | 2 | 69 | 10.14 |