Neural Network optimization with a hybrid evolutionary method that combines Particle Swarm and Genetic Algorithms with fuzzy rules

Author

Valdez, F. ; Melin, P.

Author_Institution

Univ. Autonoma de Baja California, Tijuana

fYear

2008

Firstpage

1

Lastpage

6

Abstract

We describe in this paper a new hybrid evolutionary method that combines PSO and GA with fuzzy rules for the optimization of the topology of a Neural Network (NN) for the problem of face recognition. In this case, we used the Yale face database for training the Neural Network. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible.

Keywords

face recognition; fuzzy logic; genetic algorithms; neural nets; particle swarm optimisation; topology; Yale face database; face recognition; fuzzy logic; fuzzy rules; genetic algorithms; hybrid evolutionary method; neural network optimization; particle swarm optimization; topology; Acceleration; Databases; Face recognition; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Network topology; Neural networks; Optimization methods; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American

Conference_Location

New York City, NY

Print_ISBN

978-1-4244-2351-4

Electronic_ISBN

978-1-4244-2352-1

Type

conf

DOI

10.1109/NAFIPS.2008.4531335

Filename

4531335