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

Researchers propose GESR, a genetic programming method that uses BERT language models to intelligently guide mutations and crossovers in symbolic regression tasks, rather than relying on random evolutionary processes. The approach significantly improves computational efficiency compared to traditional genetic programming algorithms while maintaining strong performance across multiple regression problems.

GESR addresses a fundamental limitation in genetic programming: the inefficiency of purely random mutations and crossovers. While genetic algorithms have successfully mimicked natural evolution for decades, their indiscriminate generation of both beneficial and detrimental variations wastes computational resources. The researchers' innovation uses two BERT models as intelligent guides—one predicting optimal gene mutations through masked language modeling and another identifying optimal crossover points—essentially automating the discovery of better evolutionary pathways.

Symbolic regression, the process of discovering mathematical equations that explain empirical data, remains computationally expensive using traditional GP approaches. This challenge has grown more relevant as machine learning practitioners seek interpretable alternatives to black-box neural networks. The ability to generate human-readable mathematical formulas from data holds value across scientific research, engineering optimization, and financial modeling.

The practical impact of GESR extends to research institutions and companies investing in automated discovery systems. Improved computational efficiency directly reduces training time and computational costs, making symbolic regression more accessible for resource-constrained environments. The integration of transformer models with evolutionary algorithms represents a broader trend of hybrid AI approaches combining the interpretability of classical methods with modern deep learning capabilities.

Future developments will likely focus on scaling GESR to more complex datasets and mathematical domains. The method's generalizability across different problem types remains to be validated extensively. Competition from other hybrid approaches combining neural networks with symbolic reasoning will test whether BERT-guided evolution provides genuine advantages beyond traditional methods optimized for speed.

• →GESR uses BERT models to intelligently guide genetic mutations and crossovers, replacing random evolutionary processes with informed decisions.
• →The approach demonstrates significant computational efficiency improvements over traditional genetic programming algorithms.
• →Symbolic regression bridges interpretability and accuracy, offering advantages over black-box machine learning models.
• →Hybrid methods combining transformers with evolutionary algorithms represent an emerging trend in AI research.
• →The technique has potential applications across scientific discovery, engineering optimization, and financial modeling domains.