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Automated Discovery of Symbolic Approximation Formulae using Genetic Programming

by Mohamed M. Khatib
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 13
Year of Publication: 2020
Authors: Mohamed M. Khatib
10.5120/ijca2020920053

Mohamed M. Khatib . Automated Discovery of Symbolic Approximation Formulae using Genetic Programming. International Journal of Computer Applications. 176, 13 ( Apr 2020), 29-34. DOI=10.5120/ijca2020920053

@article{ 10.5120/ijca2020920053,
author = { Mohamed M. Khatib },
title = { Automated Discovery of Symbolic Approximation Formulae using Genetic Programming },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2020 },
volume = { 176 },
number = { 13 },
month = { Apr },
year = { 2020 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number13/31262-2020920053/ },
doi = { 10.5120/ijca2020920053 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:25.646998+05:30
%A Mohamed M. Khatib
%T Automated Discovery of Symbolic Approximation Formulae using Genetic Programming
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 13
%P 29-34
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes the use of genetic programming to automate the discovery of symbolic approximation formulae. Results are presented involving discovery of numeric approximation formulae to common functions, which are compared to Padé approximations obtained through a symbolic mathematics package. Based on these results, we consider genetic programming to be a powerful and effective technique for the automated discovery of symbolic approximation formulae.

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Index Terms

Computer Science
Information Sciences

Keywords

Genetic Programming Padé approximations Symbolic Regression

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