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Genetic Programming with Memory For Financial Trading

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Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

A memory-enabled program representation in strongly-typed Genetic Programming (GP) is compared against the standard representation in a number of financial time-series modelling tasks. The paper first presents a survey of GP systems that utilise memory. Thereafter, a number of simulations show that memory-enabled programs generalise better than their standard counterparts in most datasets of this problem domain.

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Acknowledgement

This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant Number 08/SRC/FM1389.

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Authors and Affiliations

  1. School of Computer Science and Informatics, University College Dublin, Dublin, Ireland

    Alexandros Agapitos

  2. School of Business, University College Dublin, Dublin, Ireland

    Anthony Brabazon & Michael O’Neill

Authors
  1. Alexandros Agapitos
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  2. Anthony Brabazon
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  3. Michael O’Neill
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Corresponding author

Correspondence to Alexandros Agapitos .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Agapitos, A., Brabazon, A., O’Neill, M. (2016). Genetic Programming with Memory For Financial Trading. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_2

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