Generalized Function Analysis Using Hybrid
Evolutionary Algorithms
C. Hafner, and J. Frohlich. Proceedings of the Congress on Evolutionary
Computation, 1, page 287--294. Mayflower Hotel, Washington D.C., USA, IEEE Press, (6-9 July 1999)
Abstract
Two novel codes for the prediction of time series are
presented. Unlike most of the prominent codes based on
finding a process that predicts the future data, these
codes are based on function analysis and symbolic
regression. Both codes are based on a generalization
and combination of series expansions, parameter
optimization techniques, and genetic programming. These
highly complex codes are outlined and applied to
different examples of physics and economy.
Proceedings of the Congress on Evolutionary
Computation
year
1999
month
6-9 July
pages
287--294
publisher
IEEE Press
volume
1
organisation
Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE
publisher_address
445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA
size
8 pages
isbn
0-7803-5537-7 (Microfiche)
notes
CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143 Extrapolation.
GCP v. EGP. Sunspot, Dow Jones, stock price prediction.
Full Binary trees of depth 3.
%0 Conference Paper
%1 hafner:1999:GFAUHEA
%A Hafner, Christian
%A Frohlich, Jurg
%B Proceedings of the Congress on Evolutionary
Computation
%C Mayflower Hotel, Washington D.C., USA
%D 1999
%E Angeline, Peter J.
%E Michalewicz, Zbyszek
%E Schoenauer, Marc
%E Yao, Xin
%E Zalzala, Ali
%I IEEE Press
%K algorithms, analysis, codes, complex computation, data, economy evolutionary expansions, function future generalized genetic highly hybrid optimization parameter physics, prediction, programming, prominent regression, series series, symbolic techniques, time
%P 287--294
%T Generalized Function Analysis Using Hybrid
Evolutionary Algorithms
%U http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf
%V 1
%X Two novel codes for the prediction of time series are
presented. Unlike most of the prominent codes based on
finding a process that predicts the future data, these
codes are based on function analysis and symbolic
regression. Both codes are based on a generalization
and combination of series expansions, parameter
optimization techniques, and genetic programming. These
highly complex codes are outlined and applied to
different examples of physics and economy.
%@ 0-7803-5537-7 (Microfiche)
@inproceedings{hafner:1999:GFAUHEA,
abstract = {Two novel codes for the prediction of time series are
presented. Unlike most of the prominent codes based on
finding a process that predicts the future data, these
codes are based on function analysis and symbolic
regression. Both codes are based on a generalization
and combination of series expansions, parameter
optimization techniques, and genetic programming. These
highly complex codes are outlined and applied to
different examples of physics and economy.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Mayflower Hotel, Washington D.C., USA},
author = {Hafner, Christian and Frohlich, Jurg},
biburl = {https://www.bibsonomy.org/bibtex/204f487367f2448c512a354a9aed7d590/brazovayeye},
booktitle = {Proceedings of the Congress on Evolutionary
Computation},
editor = {Angeline, Peter J. and Michalewicz, Zbyszek and Schoenauer, Marc and Yao, Xin and Zalzala, Ali},
interhash = {693c511e215b7704332ed0583393c6bf},
intrahash = {04f487367f2448c512a354a9aed7d590},
isbn = {0-7803-5537-7 (Microfiche)},
keywords = {algorithms, analysis, codes, complex computation, data, economy evolutionary expansions, function future generalized genetic highly hybrid optimization parameter physics, prediction, programming, prominent regression, series series, symbolic techniques, time},
month = {6-9 July},
notes = {CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143 Extrapolation.
GCP v. EGP. Sunspot, Dow Jones, stock price prediction.
Full Binary trees of depth 3.},
organisation = {Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE},
pages = {287--294},
publisher = {IEEE Press},
publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA},
size = {8 pages},
timestamp = {2008-06-19T17:40:48.000+0200},
title = {Generalized Function Analysis Using Hybrid
Evolutionary Algorithms},
url = {http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf},
volume = 1,
year = 1999
}