Hybrid single node genetic programming for symbolic regression

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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
49 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationTransactions on Computational Collective Intelligence XXIV
EditorsNT Nguyen, R Kowalczyk, J Filipe
Place of PublicationBerlin, Germany
VolumeLNCS 9770
ISBN (Print)9783662535240
Publication statusPublished - 2016
Event7th International Joint Conference on Computational Intelligence - Lisbon, Portugal
Duration: 12 Nov 201514 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9770 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference7th International Joint Conference on Computational Intelligence
Abbreviated titleIJCCI 2015

Bibliographical note

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


  • Genetic programming
  • Single node genetic programming
  • Symbolic regression


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