#genetic-programming News & Analysis

6 articles tagged with #genetic-programming. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · 6d ago7/10

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Learning to Solve and Optimize by Evolving Code

Researchers introduce CHECKMATE, a tool that automatically generates optimization algorithms through code evolution, requiring only formal problem specifications and natural language descriptions rather than expert-designed heuristics. The evolved algorithms outperform state-of-the-art solvers on industrial configuration and scheduling problems, demonstrating formal methods can guide automated algorithm discovery for complex real-world optimization challenges.

AIBearisharXiv – CS AI · 2d ago6/10

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Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution

Researchers demonstrate that Large Language Models exhibit systematic convergence bias when mutating programs, revisiting similar structural forms in 87% of cases despite stochastic variation. This reveals a fundamental tension in LLM-driven program evolution: while these models excel at semantics-aware transformations, they inherently constrain exploration toward restricted regions of program space, limiting their effectiveness for open-ended evolutionary search.

AINeutralarXiv – CS AI · May 296/10

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Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis

Researchers propose using genetic programming to evolve interpretable feature sets and tree structures for survival analysis models, demonstrating improved predictive performance while maintaining shallow, explainable decision trees. The approach addresses the fundamental trade-off between accuracy and interpretability in medical survival prediction by optimizing both feature construction and tree logic simultaneously.

AINeutralarXiv – CS AI · May 126/10

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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.

AINeutralarXiv – CS AI · Apr 146/10

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Teaching the Teacher: The Role of Teacher-Student Smoothness Alignment in Genetic Programming-based Symbolic Distillation

Researchers propose a novel framework for improving symbolic distillation of neural networks by regularizing teacher models for functional smoothness using Jacobian and Lipschitz penalties. This approach addresses the core challenge that standard neural networks learn complex, irregular functions while symbolic regression models prioritize simplicity, resulting in poor knowledge transfer. Results across 20 datasets demonstrate statistically significant improvements in predictive accuracy for distilled symbolic models.

AINeutralarXiv – CS AI · Mar 54/10

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Physics-constrained symbolic regression for discovering closed-form equations of multimodal water retention curves from experimental data

Researchers developed a physics-constrained machine learning framework that uses genetic programming to automatically discover closed-form mathematical equations for modeling water retention in porous materials with complex pore structures. The approach represents mathematical expressions as binary trees and incorporates physical constraints to ensure scientifically valid solutions.