An improved single node genetic programming for symbolic regression

Jiří Kubaĺýk, R. Babuska

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

1 Citation (Scopus)

Abstract

This paper presents a first step of our research on designing an effective and efficient GP-based method for solving the symbolic regression. We have proposed three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on the depth of the nodes, (2) operators for placing a compact version of the best tree to the beginning and to the end of the population, and (3) a local search strategy with multiple mutations applied in each iteration. All the proposed modifications have been experimentally evaluated on three symbolic regression problems and compared with standard GP and SNGP. The achieved results are promising showing the potential of the proposed modifications to significantly improve the performance of the SNGP algorithm.

Original languageEnglish
Title of host publicationProceedings of the 7th International Joint Conference on Computational Intelligence
EditorsAgostinho Rosa, Juan Julian Merelo, Antonio Dourado, Jose M. Cadenas, Kurosh Madani, Antonio Ruano, Joaquim Filipe
Place of PublicationPiscataway, NJ, USA
PublisherIEEE Society
Pages244-251
Volume1: ECTA
ISBN (Print)978-989-758-165-6
Publication statusPublished - 2015
Event7th International Joint Conference on Computational Intelligence - Lisbon, Portugal
Duration: 12 Nov 201514 Nov 2015

Conference

Conference7th International Joint Conference on Computational Intelligence
Abbreviated titleIJCCI 2015
CountryPortugal
CityLisbon
Period12/11/1514/11/15

Keywords

  • Genetic Programming
  • Single Node Genetic Programming
  • Symbolic Regression

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