Title :
Generalized function analysis using hybrid evolutionary algorithms
Author :
Hafner, Christian ; Fröhlich, Jürg
Author_Institution :
Electromagn. Group, Fed. Inst. of Technol., Zurich, Switzerland
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
Keywords :
codes; data handling; evolutionary computation; prediction theory; time series; economy; future data; generalized function analysis; genetic programming; highly complex codes; hybrid evolutionary algorithms; parameter optimization techniques; physics; prominent codes; series expansions; symbolic regression; time series prediction; Algorithm design and analysis; Electromagnetics; Evolutionary computation; Extrapolation; Fourier series; Frequency; Linear systems; Neural networks; Nonlinear equations; Physics;
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
DOI :
10.1109/CEC.1999.781938
