Abstract
In this paper, the stock index S&P 500 is used to test the predicting performance of genetic programming (GP) and genetic programming neural networks (GPNN). While both GP and GPNN are considered universal approximators, in this practical financial application, they perform differently. GPNN seemed to suffer the overlearning problem more seriously than GP; the latter outdid the former in all the simulations.
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References
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© 1998 Springer-Verlag Wien
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Chen, SH., Ni, CC. (1998). Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_87
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_87
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive