Generalized function analysis using hybrid evolutionary algorithms
Hafner, C.
Frohlich, J.
Electromagn. Group, Fed. Inst. of Technol., Zurich ;
This paper appears in: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Publication Date: 1999
Volume: 1,
On page(s): -294 Vol. 1
Meeting Date: 07/06/1999 - 07/09/1999
Location: Washington, DC, USA
ISBN: 0-7803-5536-9
References Cited: 5
INSPEC Accession Number: 6338855
Digital Object Identifier: 10.1109/CEC.1999.781938
Current Version Published: 2002-08-06
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
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