Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data

Title	Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data
Creator	陳樹衡 Chen, Shu-Heng Ni, C.-C.
Contributor	經濟學系學
Date	1998
Date Issued	31-Jan-2019 13:44:43 (UTC+8)
Summary	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 universalapproximators, 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.
Relation	Artificial Neural Nets and Genetic Algorithms pp 397-400
Type	book/chapter
DOI	https://doi.org/10.1007/978-3-7091-6492-1_87

dc.contributor	經濟學系學	zh_TW
dc.creator (作者)	陳樹衡	zh_TW
dc.creator (作者)	Chen, Shu-Heng	en_US
dc.creator (作者)	Ni, C.-C.	en_US
dc.date (日期)	1998	-
dc.date.accessioned	31-Jan-2019 13:44:43 (UTC+8)	-
dc.date.available	31-Jan-2019 13:44:43 (UTC+8)	-
dc.date.issued (上傳時間)	31-Jan-2019 13:44:43 (UTC+8)	-
dc.identifier.uri (URI)	http://nccur.lib.nccu.edu.tw/handle/140.119/122238	-
dc.description.abstract (摘要)	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 universalapproximators, 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.	en_US
dc.format.extent	691553 bytes	-
dc.format.mimetype	application/pdf	-
dc.relation (關聯)	Artificial Neural Nets and Genetic Algorithms pp 397-400	en_US
dc.title (題名)	Evolutionary Artificial Neural Networks and Genetic Programming: A Comparative Study Based on Financial Data	en_US
dc.type (資料類型)	book/chapter	-
dc.identifier.doi (DOI)	10.1007/978-3-7091-6492-1_87	-
dc.doi.uri (DOI)	https://doi.org/10.1007/978-3-7091-6492-1_87	-