arXiv:2602.10233v2 Announce Type: replace-cross Abstract: LLM-guided evolutionary computation, most notably AlphaEvolve, has been remarkably successful in discovering novel mathematical constructions by solving challenging optimization problems. The standard approach is to evolve a monolithic program that directly outputs a candidate solution. We present ImprovEvolve, an algorithmic alternative that drastically reduces cognitive load on the LLM. Instead of prompting the model for an end-to-end optimizer, we evolve a program with three specialized operators of initialization, local improvement,
The continuous evolution of Large Language Models (LLMs) and their application in problem-solving necessitates more efficient integration methods, leading to innovations like ImprovEvolve. This work builds on the success of earlier LLM-guided evolutionary computation.
This development represents a significant step in optimizing LLM utility for complex problem-solving, reducing the computational and cognitive load associated with creating end-to-end optimizers. It will accelerate the discovery of novel mathematical constructions and solutions to challenging optimization problems.
Instead of full end-to-end programming, LLMs will be guided to evolve specialized operators for initialization, local improvement, and perturbation, making their application in evolutionary computation more modular and efficient. This modular approach significantly lowers the barrier for designing sophisticated problem-solvers.
- · AI researchers
- · Optimization software developers
- · Sectors relying on complex optimization (e.g., materials science, drug discovery
- · Developers of monolithic AI optimization systems
The new algorithmic approach, ImprovEvolve, enhances the efficiency of LLM-guided evolutionary computation for solving complex optimization problems.
This improved efficiency could lead to a faster pace of scientific discovery and technological innovation across various fields by making advanced problem-solving more accessible.
The modularity could foster new paradigms for human-AI collaboration in algorithm design, allowing domain experts to more easily customize and deploy AI-driven problem-solvers.
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Read at arXiv cs.AI