Hybrid single node genetic programming for symbolic regression

Hybrid single node genetic programming for symbolic regression

Kubalìk, Jiřì (Czech Technical University)
Alibekov, Eduard (Czech Technical University)
Žegklitz, Jan (Czech Technical University)
Babuska, R. (TU Delft OLD Intelligent Control & Robotics; Czech Technical University)

Nguyen, NT (editor)
Kowalczyk, R (editor)
Filipe, J (editor)

2016

This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact version of the best-performing graph to the beginning and to the end of the population, respectively, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on five symbolic regression benchmarks and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to improve the performance of the SNGP algorithm. We then propose two variants of hybrid SNGP utilizing a linear regression technique, LASSO, to improve its performance. The proposed algorithms have been compared to the state-of-the-art symbolic regression methods that also make use of the linear regression techniques on four real-world benchmarks. The results show the hybrid SNGP algorithms are at least competitive with or better than the compared methods.

Genetic programming
Single node genetic programming
Symbolic regression

http://resolver.tudelft.nl/uuid:9af142d7-f792-4638-a3aa-f9b0447c0b06

https://doi.org/10.1007/978-3-662-53525-7_4

Springer, Berlin, Germany

9783662535240

Transactions on Computational Collective Intelligence XXIV, LNCS 9770

7th International Joint Conference on Computational Intelligence, 2015-11-12 → 2015-11-14, Lisbon, Portugal

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 03029743, 9770 LNCS

Accepted Author Manuscript. Revised version of a selected paper from IJCCI 2015.

Institutional Repository

conference paper

© 2016 Jiřì Kubalìk, Eduard Alibekov, Jan Žegklitz, R. Babuska