| Title |
Hybrid evolutionary algorithm with product-unit neural networks for classification |
| Authors |
MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Hervás-Martínez C. , MARTÍNEZ ESTUDILLO, ALFONSO CARLOS, Gutiérrez-Peña P.A. |
| External publication |
No |
| Means |
Lect. Notes Comput. Sci. |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.29300 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049151009&partnerID=40&md5=8e016c388ebe3a4aee94140a80d4e09f |
| Publication date |
01/01/2007 |
| Scopus Id |
2-s2.0-38049151009 |
| Abstract |
In this paper we propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks, and on a dynamic version of a hybrid evolutionary neural network algorithm. The method combines an evolutionary algorithm, a clustering process, and a local search procedure, where the clustering process and the local search are only applied at specific stages of the evolutionary process. Our results with the product-unit models and the evolutionary approach show a very interesting performance in terms of classification accuracy, yielding a state-of-the-art performance. © Springer-Verlag Berlin Heidelberg 2007. |
| Keywords |
Classification (of information); Clustering algorithms; Neural networks; Product-unit neural networks; Evolutionary algorithms |
| Universidad Loyola members |
|
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