Theses and Dissertations

ORCID

0009-0008-4955-0122

Advisor

Bhushan, Shanti

Committee Member

Dash, Padmanava

Committee Member

Burgreen, Greg

Date of Degree

8-12-2026

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Computational Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Computational Engineering Program

Abstract

This research explores application of symbolic regression using genetic programming (SRGP) to develop interpretable analytical models. Most state-of-the-art machine learning techniques produce models with reasonable predictive accuracy. However, the black-box nature of these models occludes the relationship between inputs and outputs. Symbolic regression using genetic programming evolves an analytical equation that is transparent, interpretable, and portable, allowing researchers to easily apply the models on the field with minimal equipment and inputs. This study demonstrates the capability of genetic programming to generate accurate and explainable models, highlighting its potential as an alternative to traditional machine learning approaches where interpretability is an important requirement. SRGP is demonstrated in two cases. The first case consists of lift of an aerodynamic vehicle as a function of varying Mach number, altitude and angle of attack conditions. The second case consists of chlorophyll-a concentration in water bodies as a function of multi-spectral reflectance values.

Sponsorship (Optional)

This research was supported by federal funding from NASA EPSCoR (Award No. G00007006); the Mississippi Department of Environmental Quality and the U.S. Department of the Treasury through the Resources and Ecosystems Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act) (Award Nos. 8006490-02.01 MSU and 8007148-02.01 MSU); and the U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA (Award No. W912HZ-19-2-0019). The statements, findings, conclusions, and recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of NASA EPSCoR, the U.S. Army Engineer Research and Development Center, the Mississippi Department of Environmental Quality, or the U.S. Department of the Treasury.

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