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A Vectorial Approach to Genetic Programming

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Genetic Programming (EuroGP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11451))

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Abstract

Among the various typologies of problems to which Genetic Programming (GP) has been applied since its origins, symbolic regression is one of the most popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a panel representation where each observation corresponds to a collection on a subject at a precise time instant. However, representing data in this form may imply a loss of information: for instance, the algorithm may not be able to recognize observations belonging to the same subject and their recording order. To maintain the source of knowledge supplied by ordered sequences as time series, we propose a new approach to GP that keeps instances of the same observation together in a vector, introducing vectorial variables as terminals. This new representation allows aggregate functions in the primitive GP set, included with the purpose of describing the behaviour of vectorial variables. In this work, we perform a comparative analysis of vectorial GP (VE-GP) against standard GP (ST-GP). Experiments are conducted on different benchmark problems to highlight the advantages of this new approach.

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Acknowledgments

This work was partially supported by FCT through funding of LASIGE Research Unit (UID/CEC/00408/2019), BioISI Research Unit (UID/MULTI/04046/2013), and projects INTERPHENO (PTDC/ASP-PLA/28-726/2017), PERSEIDS (PTDC/EMS-SIS/0642/2014), OPTOX (PTDC/CTA-AMB/30056/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DS-AIPA/DS/0022/2018) and PREDICT (PTDC/CCI-CIF/29877/2017).

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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 & Illya Bakurov

  3. LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal

    Sara Silva

  4. BioISI – Biosystems & Integrative Sciences Institute, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal

    Sara Silva

Authors
  1. Irene Azzali
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  2. Leonardo Vanneschi
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  3. Sara Silva
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  4. Illya Bakurov
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  5. Mario Giacobini
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Corresponding author

Correspondence to Irene Azzali .

Editor information

Editors and Affiliations

  1. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  2. Memorial University, St. John's, NL, Canada

    Ting Hu

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. HTWK Leipzig University of Applied Sciences, Leipzig, Germany

    Hendrik Richter

  5. University of Granada, Granada, Spain

    Pablo García-Sánchez

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Azzali, I., Vanneschi, L., Silva, S., Bakurov, I., Giacobini, M. (2019). A Vectorial Approach to Genetic Programming. In: Sekanina, L., Hu, T., Lourenço, N., Richter, H., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2019. Lecture Notes in Computer Science(), vol 11451. Springer, Cham. https://doi.org/10.1007/978-3-030-16670-0_14

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  • DOI: https://doi.org/10.1007/978-3-030-16670-0_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16669-4

  • Online ISBN: 978-3-030-16670-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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