Evolving artificial neural networks

Author

Yao, Xin

Author_Institution

Sch. of Comput. Sci., Birmingham Univ., UK

Volume

87

Issue

9

fYear

1999

fDate

9/1/1999 12:00:00 AM

Firstpage

1423

Lastpage

1447

Abstract

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone

Keywords

genetic algorithms; learning (artificial intelligence); neural nets; search problems; technological forecasting; connection weights; evolutionary algorithms; intelligent systems; learning; neural networks; search operators; Adaptive systems; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Competitive intelligence; Computer networks; Evolutionary computation; Intelligent networks; Intelligent systems; Transfer functions;

fLanguage

English

Journal_Title

Proceedings of the IEEE

Publisher

ieee

ISSN

0018-9219

Type

jour

DOI

10.1109/5.784219

Filename

784219