TY - GEN
T1 - A Hybrid Cooperative Approach for Symbolic Regression
AU - Etaati, Bahareh
AU - Wagner, Stefan
AU - Affenzeller, Michael
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - In multi-objective symbolic regression, the objective is to improve the model’s accuracy while minimizing its complexity. It results in a Pareto front, including a fair compromise between accuracy and complexity. In this study, we propose a hybrid cooperative genetic programming approach containing the hybridization of NSGA-II and an adaptive weighted multi-population GA, which cooperatively optimize both models’ accuracy and tree length. In the weighted multi-population GA, the weights are assigned adaptively. We also propose a new version of offspring selection to suit the needs of multi-objective symbolic regression. The two algorithms communicate solutions with each other in specific intervals. The proposed algorithm is tested on the Feynman benchmark datasets, and the results are comparable to the NSGA-II in terms of accuracy and models’ tree length.
AB - In multi-objective symbolic regression, the objective is to improve the model’s accuracy while minimizing its complexity. It results in a Pareto front, including a fair compromise between accuracy and complexity. In this study, we propose a hybrid cooperative genetic programming approach containing the hybridization of NSGA-II and an adaptive weighted multi-population GA, which cooperatively optimize both models’ accuracy and tree length. In the weighted multi-population GA, the weights are assigned adaptively. We also propose a new version of offspring selection to suit the needs of multi-objective symbolic regression. The two algorithms communicate solutions with each other in specific intervals. The proposed algorithm is tested on the Feynman benchmark datasets, and the results are comparable to the NSGA-II in terms of accuracy and models’ tree length.
KW - Genetic Programming
KW - Multi-objective Optimization
KW - NSGA-II
KW - Symbolic Regression
UR - https://www.scopus.com/pages/publications/105004253583
U2 - 10.1007/978-3-031-82949-9_6
DO - 10.1007/978-3-031-82949-9_6
M3 - Conference contribution
AN - SCOPUS:105004253583
SN - 9783031829512
T3 - Lecture Notes in Computer Science
SP - 53
EP - 67
BT - Computer Aided Systems Theory - EUROCAST 2024 - 19th International Conference, 2024, Revised Selected Papers
A2 - Quesada-Arencibia, Alexis
A2 - Affenzeller, Michael
A2 - Moreno-Díaz, Roberto
PB - Springer
T2 - 19th International Conference on Computer Aided Systems Theory, EUROCAST 2024
Y2 - 25 February 2024 through 1 March 2024
ER -