Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

Irene Azzali, Leonardo Vanneschi, Mario Giacobini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationGenetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings
EditorsTing Hu, Nuno Lourenço, Eric Medvet, Federico Divina
PublisherSpringer
Pages52-67
Number of pages16
ISBN (Print)9783030440930
DOIs
Publication statusPublished - 1 Jan 2020
Event23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020 - Seville, Spain
Duration: 15 Apr 202017 Apr 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12101 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020
CountrySpain
CitySeville
Period15/04/2017/04/20

Keywords

  • Geometric semantic operators
  • Sliding windows
  • Time series
  • Vector-based genetic programming

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  • Cite this

    Azzali, I., Vanneschi, L., & Giacobini, M. (2020). Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12101 LNCS). Springer. https://doi.org/10.1007/978-3-030-44094-7_4