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European Conference on Genetic Programming

EuroGP 2003: Genetic Programming pp 34–46Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2610))

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 overfittingavoidance 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.

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Author information

Authors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, 11623, Taiwan, Taipei

    Chen Shu-Heng & Kuo Tzu-Wen

Authors
  1. Chen Shu-Heng
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  2. Kuo Tzu-Wen
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Editor information

Editors and Affiliations

  1. Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Conor Ryan

  2. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    Terence Soule

  3. Department of Computer Science, Free University of Amsterdam, 1081, HV Amsterdam, The Netherlands

    Maarten Keijzer

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Edward Tsang  & Riccardo Poli  & 

  5. Department of Computer Science, University of Coimbra, Polo II, Pinhal Marrocos, 3030-290, Coimbra, Portugal

    Ernesto Costa

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© 2003 Springer-Verlag Berlin Heidelberg

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Shu-Heng, C., Tzu-Wen, K. (2003). Overfitting or Poor Learning: A Critique of Current Financial Applications of GP. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_4

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  • DOI: https://doi.org/10.1007/3-540-36599-0_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00971-9

  • Online ISBN: 978-3-540-36599-0

  • eBook Packages: Springer Book Archive

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