TY - GEN
T1 - Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming
AU - Azzali, Irene
AU - Vanneschi, Leonardo
AU - Giacobini, Mario
N1 - 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
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Geometric semantic operators
KW - Sliding windows
KW - Time series
KW - Vector-based genetic programming
UR - http://www.scopus.com/inward/record.url?scp=85084761821&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-44094-7_4
DO - 10.1007/978-3-030-44094-7_4
M3 - Conference contribution
AN - SCOPUS:85084761821
SN - 9783030440930
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 52
EP - 67
BT - Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings
A2 - Hu, Ting
A2 - Lourenço, Nuno
A2 - Medvet, Eric
A2 - Divina, Federico
PB - Springer
T2 - 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020
Y2 - 15 April 2020 through 17 April 2020
ER -