This research introduces a novel end-to-end approach that revolutionizes how LLMs generate algorithms by simultaneously evolving structural design and functional components.

• Template-Free Generation: Eliminates dependency on predefined templates and expert-specified functions
• Global Optimization: Optimizes the entire algorithm architecture instead of just local functionalities
• Co-Evolution Framework: Creates synergy between structure and function for higher-quality algorithms
• Practical Engineering Applications: Enables more powerful, versatile algorithm design across diverse domains

This breakthrough matters for engineering teams because it represents a significant step toward fully automated, high-performance algorithm design that requires less human expertise and intervention.

From Understanding to Excelling: Template-Free Algorithm Design through Structural-Functional Co-Evolution