%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 }