Yet another genetic algorithm for feed-forward neural networks

Author

Neruda, Roman

Author_Institution

Inst. of Comput. Sci., Czechoslovak Acad. of Sci., Prague, Czech Republic

fYear

1997

Firstpage

375

Lastpage

380

Abstract

A functional equivalence property of feedforward networks has been proposed to reduce the search space of learning algorithms. We summarize previous results, describing the form of functional equivalence for one-hidden-layer perceptron networks and radial basis function (RBF) networks with Gaussians. The description of equivalence classes is used in a proposition of a genetic learning algorithm which is tested on two simple problems and which outperforms the standard genetic learning procedure

Keywords

equivalence classes; feedforward neural nets; genetic algorithms; learning (artificial intelligence); perceptrons; search problems; software performance evaluation; Gaussians; equivalence classes; feedforward neural networks; functional equivalence property; genetic algorithm; genetic learning procedure; learning algorithm; perceptrons; performance; radial basis function networks; search space reduction; Computer networks; Computer science; Feedforward neural networks; Feedforward systems; Gaussian processes; Genetic algorithms; Neural networks; Radial basis function networks; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International Conference on

Conference_Location

Newport Beach, CA

ISSN

1082-3409

Print_ISBN

0-8186-8203-5

Type

conf

DOI

10.1109/TAI.1997.632278

Filename

632278