GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing

📰 ArXiv cs.AI

Learn how GESR, a genetic programming-based symbolic regression method, uses gene editing to discover mathematical laws from scientific data

advanced Published 12 May 2026

Action Steps

Apply genetic programming to a symbolic regression problem using GESR
Use gene editing to modify and improve the discovered mathematical formulas
Evaluate the performance of GESR on a benchmark dataset
Compare the results of GESR with other symbolic regression methods
Implement GESR in a real-world problem to discover new mathematical laws

Who Needs to Know This

Data scientists and researchers working on symbolic regression problems can benefit from this method to improve their model discovery capabilities. This can be particularly useful in interdisciplinary teams where communication between humans and nature is crucial.

Key Insight

💡 GESR uses gene editing to improve the discovery of mathematical laws from scientific data, outperforming traditional genetic programming methods

Key Takeaways

Learn how GESR, a genetic programming-based symbolic regression method, uses gene editing to discover mathematical laws from scientific data

Full Article

Title: GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing

Abstract:
arXiv:2605.10685v1 Announce Type: new Abstract: Mathematical formulas serve as a language through which humans communicate with nature. Discovering mathematical laws from scientific data to describe natural phenomena has been a long-standing pursuit of humanity for centuries. In the field of artificial intelligence, this challenge is known as the symbolic regression problem. Among existing symbolic regression approaches, Genetic Programming (GP) based on evolutionary algorithms remains one of th

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