DocumentCode
3256415
Title
Evolving recurrent neural networks with non-binary encoding
Author
Mandischer, Martin
Author_Institution
Syst. Anal. Res. Group, Dortmund Univ., Germany
Volume
2
fYear
1995
fDate
29 Nov-1 Dec 1995
Firstpage
584
Abstract
This paper presents an evolutionary approach for the design of feedforward and recurrent neural networks. We show that evolutionary algorithms can be used for the construction of networks for real-world tasks. Therefore, a data structure based genotypic network representation, as well as genetic operators, are introduced. Results from the classification, function approximation and time-series domains are presented
Keywords
data structures; encoding; feedforward neural nets; function approximation; genetic algorithms; pattern classification; recurrent neural nets; time series; classification; data structure based genotypic network representation; evolutionary algorithms; evolutionary design approach; feedforward neural networks; function approximation; genetic operators; nonbinary encoding; real-world tasks; recurrent neural networks; time series; Computer science; Electronic mail; Encoding; Evolutionary computation; Feedforward systems; Function approximation; Genetic algorithms; Network topology; Neural networks; Recurrent neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 1995., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2759-4
Type
conf
DOI
10.1109/ICEC.1995.487449
Filename
487449
Link To Document