A GRASP approach for Symbolic Regression

Raka Jovanovic, Sahel Ashhab

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper a metaheuristic approach is proposed for solving the problem of symbolic regression for function approximation. The focus is on developing a method that is easy to implement and can be used to generate initial populations for more advanced metaheuristics. This is achieved by first developing a greedy heuristic which expands (adds terms) generated formulas while increasing the quality of the approximation. This basic algorithm is extended to the Greedy randomized adaptive search procedure (GRASP) by adding randomization and a local search. The local search consists in removing unnecessary terms from the generated formulas. The performed computational experiments show that the GRASP approach, in case of grammars having a limited number of terminal symbols, substantially out performs algorithms based on the artificial bee colony algorithm and ant colony optimization.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1723-1728
Number of pages6
ISBN (Electronic)9781728124858
DOIs
Publication statusPublished - Dec 2019
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
CountryChina
CityXiamen
Period6/12/199/12/19

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Keywords

  • GRASP
  • function approximation
  • symbolic regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Modelling and Simulation

Cite this

Jovanovic, R., & Ashhab, S. (2019). A GRASP approach for Symbolic Regression. In 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 (pp. 1723-1728). [9003035] (2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI44817.2019.9003035