Name
Abstract | ||
|---|---|---|
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 values can lead to take advantage of the metaheuristic capabilities to solve the problem at hand. Tuning strategies are step by step methods based on multiple runs of the metaheuristic algorithm. In this study we compare four automated tuning methods: F-Race, Revac, ParamILS and SPO. We evaluate the performance of each method using a standard genetic algorithm for continuous function optimization. We discuss about the requirements 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2013.12.017 | Appl. Soft Comput. |
Keywords | Field | DocType |
standard genetic algorithm,hard optimization problem,metaheuristic method,tuning strategy,automated tuning method,metaheuristic capability,metaheuristic algorithm,continuous function optimization,step method,appropriate tuning procedure,evolutionary algorithms,metaheuristics | Continuous function,Mathematical optimization,Evolutionary algorithm,Parallel metaheuristic,Computer science,Artificial intelligence,Optimization problem,Machine learning,Genetic algorithm,Metaheuristic | Journal |
Volume | ISSN | Citations |
17, | 1568-4946 | 14 |
PageRank | References | Authors |
0.61 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Montero | 1 | 69 | 10.14 |
| María Cristina Riff | 2 | 200 | 23.91 |
| Bertrand Neveu | 3 | 253 | 23.18 |