Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

Bibliographic Details
Main Author: Azzali, Irene
Publication Date: 2020
Other Authors: Vanneschi, Leonardo, Giacobini, Mario
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/142267
Summary: 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 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).
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spelling Investigating the Use of Geometric Semantic Operators in Vectorial Genetic ProgrammingGeometric semantic operatorsSliding windowsTime seriesVector-based genetic programmingTheoretical Computer ScienceComputer Science(all)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 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).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.SpringerNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNAzzali, IreneVanneschi, LeonardoGiacobini, Mario2022-07-21T22:14:07Z2020-01-012020-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersion16application/pdfhttp://hdl.handle.net/10362/142267eng97830304409300302-9743PURE: 18321861https://doi.org/10.1007/978-3-030-44094-7_4info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-07-22T01:36:32Zoai:run.unl.pt:10362/142267Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:34:26.799504Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
title Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
spellingShingle Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
Azzali, Irene
Geometric semantic operators
Sliding windows
Time series
Vector-based genetic programming
Theoretical Computer Science
Computer Science(all)
title_short Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
title_full Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
title_fullStr Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
title_full_unstemmed Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
title_sort Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
author Azzali, Irene
author_facet Azzali, Irene
Vanneschi, Leonardo
Giacobini, Mario
author_role author
author2 Vanneschi, Leonardo
Giacobini, Mario
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Azzali, Irene
Vanneschi, Leonardo
Giacobini, Mario
dc.subject.por.fl_str_mv Geometric semantic operators
Sliding windows
Time series
Vector-based genetic programming
Theoretical Computer Science
Computer Science(all)
topic Geometric semantic operators
Sliding windows
Time series
Vector-based genetic programming
Theoretical Computer Science
Computer Science(all)
description 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 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
2020-01-01T00:00:00Z
2022-07-21T22:14:07Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/142267
url http://hdl.handle.net/10362/142267
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 9783030440930
0302-9743
PURE: 18321861
https://doi.org/10.1007/978-3-030-44094-7_4
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 16
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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