Name
Abstract | ||
|---|---|---|
In this work a new algorithm to improve the performance of opti- mization methods, by means of avoiding certain local optima, is described. Its theoretical bases are presented in a rigorous, but intuitive, way. It has been ap- plied concretely to the case of recurrent neural networks, in particular to MREM, a multivalued recurrent model, that has proved to obtain very good results when dealing with NP-complete combinatorial optimization problems. In order to show its efficiency, the well-known MaxCut problem for graphs has been selected as ben- chmark. Our proposal outperforms other specialized and powerful techniques, as shown by simulations. |
Year | Venue | DocType |
|---|---|---|
2006 | ESANN | Conference |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enrique Mérida Casermeiro | 1 | 22 | 5.38 |
| Domingo López-Rodríguez | 2 | 55 | 9.24 |
| Juan Miguel Ortiz-de-lazcano-lobato | 3 | 68 | 11.59 |