Automatic design of cellular neural networks by means of genetic algorithms: finding a feature detector

Author

Dellaert, Frank ; Vandewalle, Joos

Author_Institution

Dept. of Comput. Eng. & Sci., Case Western Reserve Univ., Cleveland, OH, USA

fYear

1994

fDate

18-21 Dec 1994

Firstpage

189

Lastpage

194

Abstract

The paper aims to examine the use of genetic algorithms to optimize subsystems of cellular neural network architectures. The application at hand is character recognition: the aim is to evolve an optimal feature detector in order to aid a conventional classifier network to generalize across different fonts. To this end, a performance function and a genetic encoding for a feature detector are presented. An experiment is described where an optimal feature detector is indeed found by the genetic algorithm

Keywords

cellular neural nets; character recognition; computer vision; encoding; feature extraction; generalisation (artificial intelligence); genetic algorithms; neural net architecture; pattern classification; automatic design; cellular neural network architectures; cellular neural networks; character recognition; conventional classifier network; feature detector; fonts; generalisation; genetic algorithms; genetic encoding; optimal feature detector; performance function; subsystem optimisation; Algorithm design and analysis; Cellular neural networks; Character recognition; Computer vision; Detectors; Genetic algorithms; Image recognition; Neural networks; Neurons; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and their Applications, 1994. CNNA-94., Proceedings of the Third IEEE International Workshop on

Conference_Location

Rome

Print_ISBN

0-7803-2070-0

Type

conf

DOI

10.1109/CNNA.1994.381681

Filename

381681