Heuristic Search as Language-Guided Program Optimization

2026-02-19 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new structured framework for Large Language Model (LLM)-driven Automated Heuristic Design (AHD) in combinatorial optimization (CO) has been proposed to overcome limitations of existing discovery pipelines. This framework explicitly decomposes the heuristic discovery process into modular stages: a forward pass for evaluation, a backward pass for analytical feedback, and an update step for program refinement. This modular separation facilitates iterative refinement and allows for principled improvements of individual components, reducing the need for extensive manual trial-and-error or domain expertise. The framework was validated across four diverse real-world CO domains, where it consistently outperformed baselines, achieving up to a 0.17 improvement in QYI on unseen test sets. Several popular AHD methods are shown to be restricted instantiations of this framework, and integrating them within this structured pipeline significantly improves their performance through modular component upgrades.

Key takeaway

For research scientists developing LLM-driven optimization solutions, you should consider adopting a modular framework for heuristic design. This approach, by separating evaluation, feedback, and refinement, can significantly reduce manual trial-and-error and improve performance by up to 0.17 QYI. Evaluate how your current AHD methods can be refactored into these distinct stages to enable more systematic and effective iterative improvements.

Key insights

Modularizing LLM-driven heuristic design improves performance and reduces manual effort in combinatorial optimization.

Principles

• Decompose complex processes into modular stages.
• Iterative refinement enhances system performance.

Method

The proposed method decomposes LLM-driven AHD into a forward pass for evaluation, a backward pass for analytical feedback, and an update step for program refinement, enabling systematic iterative improvement.

In practice

• Apply modular decomposition to LLM-driven design.
• Integrate existing AHD methods into the framework.

Topics

• Automated Heuristic Design
• Large Language Models
• Combinatorial Optimization
• Program Optimization
• Heuristic Search

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.