Overfitting or Poor Learning: A Critique of Current Financial Applications of GP

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{chen03,

• author = "Shu-Heng Chen and Tzu-Wen Kuo",
• title = "Overfitting or Poor Learning: A Critique of Current Financial Applications of GP",
• booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
• year = "2003",
• editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa",
• volume = "2610",
• series = "LNCS",
• pages = "34--46",
• address = "Essex",
• publisher_address = "Berlin",
• month = "14-16 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-00971-X",
• DOI = "doi:10.1007/3-540-36599-0_4",
• abstract = "Motivated by a measure of predictability, this paper uses the extracted signal ratio as a measure of the degree of overfitting. With this measure, we examine the performance of one type of overfitting-avoidance design frequently used in financial applications of GP. Based on the simulation results run with the software Simple GP, we find that this design is not effective in avoiding overfitting. Furthermore, within the range of search intensity typically considered by these applications, we find that underfitting, instead of overfitting, is the more prevalent problem. This problem becomes more serious when the data is generated by a process that has a high degree of algorithmic complexity. This paper, therefore, casts doubt on the conclusions made by those early applications regarding the poor performance of GP, and recommends that changes be made to ensure progress.",
• notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003",

}

Genetic Programming entries for Shu-Heng Chen Tzu-Wen Kuo

Citations