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Hybrid evolutionary algorithm with product-unit neural networks for classification

Autores

MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Hervás-Martínez C. , MARTÍNEZ ESTUDILLO, ALFONSO CARLOS, Gutiérrez-Peña P.A.

Publicación externa

No

Medio

Lect. Notes Comput. Sci.

Alcance

Conference Paper

Naturaleza

Científica

Cuartil JCR

Cuartil SJR

Impacto SJR

0.293

Fecha de publicacion

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.

Palabras clave

Classification (of information); Clustering algorithms; Neural networks; Product-unit neural networks; Evolutionary algorithms