A Vectorial Approach to Genetic Programming

Irene Azzali, Leonardo Vanneschi, Sara Silva, Illya Bakurov, Mario Giacobini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publication22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Proceedings
EditorsNuno Lourenço, Ting Hu, Hendrik Richter, Lukas Sekanina, Pablo García-Sánchez
Place of PublicationSwitzerland
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783030166694
Publication statusPublished - 1 Jan 2019
Event22nd European Conference on Genetic Programming, EuroGP 2019, held as part of EvoStar 2019 - Leipzig, Germany
Duration: 24 Apr 201926 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11451 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd European Conference on Genetic Programming, EuroGP 2019, held as part of EvoStar 2019


  • Genetic programming
  • Panel data regression
  • Vector-based representation


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