Applications of Genetic Programming and Automatic Differentiation Algorithms in the Solution of Ordinary and Partial Differential Equations

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Ana Carolina Abreu
Ana Carolina Abreu
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Marco Aurélio Pacheco
Marco Aurélio Pacheco
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Valdir Lobão
Valdir Lobão
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Douglas Dias
Douglas Dias

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Applications of Genetic Programming and Automatic Differentiation Algorithms in the Solution of Ordinary and Partial Differential Equations

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Abstract

There is a significant number of research projects using differential equations to model important and complex problems of engineering and other scientific knowledge areas. This paper investigates the potential that computational algorithms have to determine analytical solutions for ordinary and partial differential equations. In order to do so, the evolutionary method of genetic programming and the automatic differentiation method are applied. Using the MatLab programming environment, several GPAD algorithms are developed and problems of distinct differential equations are addressed. The results are promising, with exact solutions obtained for most of the addressed equations, including ones that commercial systems could not find a symbolic solution to. The conclusion is that GPAD algorithms can be used to discover analytic solutions for ordinary differential equations and partial differential equations.

References

11 Cites in Article
  1. Yikun Huang,Yi Luo,Haomin Liu,Xiuling Lu,Jing Zhao,Yu Lei (2009). A Subcutaneously Injected SERS Nanosensor Enabled Long-term in Vivo Glucose Tracking.
  2. J Imae,Y Kikuchi,N Ohtsuki,T Kobayashi,Guisheng Zhai (2004). Design of nonlinear control systems by means of differential genetic programming.
  3. S Luke,L Panait Lexicographic parsimony pressure.
  4. S Silva (2009). Gplab a genetic programming toolbox for MatLab.
  5. D Makarov,H Metiu (2000). Using Genetic Programming to Solve the Schrödinger Equation.
  6. I Tsoulos,I Lagaris (2006). Solving differential equations with genetic programming.
  7. Waldir Lobão,Marco Pacheco,Douglas Dias,Ana Abreu (2018). Solving stochastic differential equations through genetic programming and automatic differentiation.
  8. M Fink (2007). Automatic differentiation for MatLab.
  9. Johnr. Koza (1992). Genetic programming as a means for programming computers by natural selection.
  10. L Rall (1981). Automatic Differentiation: Techniques and Applications.
  11. Andreas Griewank,Andrea Walther (2008). Evaluating Derivatives.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Ana Carolina Abreu. 2026. \u201cApplications of Genetic Programming and Automatic Differentiation Algorithms in the Solution of Ordinary and Partial Differential Equations\u201d. Global Journal of Research in Engineering - J: General Engineering GJRE-J Volume 22 (GJRE Volume 22 Issue J2): .

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Optimized ALT: Applications of genetic programming and automatic differentiation algorithms in solving differential equations.
Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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Classification
GJRE-J Classification: DDC Code: 515.353 LCC Code: QA379
Version of record

v1.2

Issue date

June 14, 2022

Language
en
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Published Article

There is a significant number of research projects using differential equations to model important and complex problems of engineering and other scientific knowledge areas. This paper investigates the potential that computational algorithms have to determine analytical solutions for ordinary and partial differential equations. In order to do so, the evolutionary method of genetic programming and the automatic differentiation method are applied. Using the MatLab programming environment, several GPAD algorithms are developed and problems of distinct differential equations are addressed. The results are promising, with exact solutions obtained for most of the addressed equations, including ones that commercial systems could not find a symbolic solution to. The conclusion is that GPAD algorithms can be used to discover analytic solutions for ordinary differential equations and partial differential equations.

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Applications of Genetic Programming and Automatic Differentiation Algorithms in the Solution of Ordinary and Partial Differential Equations

Ana Carolina Abreu
Ana Carolina Abreu
Marco Aurélio Pacheco
Marco Aurélio Pacheco
Valdir Lobão
Valdir Lobão
Douglas Dias
Douglas Dias

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