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Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

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Genetic Programming (EuroGP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12101))

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Abstract

Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.

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Acknowledgments

This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-CIF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).

Author information

Authors and Affiliations

  1. DAMU - Data Analysis and Modeling Unit, Department of Veterinary Sciences, University of Torino, Turin, Italy

    Irene Azzali & Mario Giacobini

  2. NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal

    Leonardo Vanneschi

  3. LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal

    Leonardo Vanneschi

Authors
  1. Irene Azzali
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  2. Leonardo Vanneschi
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  3. Mario Giacobini
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Corresponding author

Correspondence to Irene Azzali .

Editor information

Editors and Affiliations

  1. Queen's University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

  4. Pablo de Olavide University, Seville, Spain

    Federico Divina

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Azzali, I., Vanneschi, L., Giacobini, M. (2020). Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-44094-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44093-0

  • Online ISBN: 978-3-030-44094-7

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