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New SPO framework uses LLMs to discover adaptive search operators

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Stackelberg Program Optimization (SPO) to discover effective destroy and repair operators for large neighborhood search (LNS) algorithms. SPO utilizes an LLM-based approach to create adaptive programs that condition operator decisions on the evolving LNS state. This method organizes the discovery process as a Stackelberg interaction, where destroy operators act as leaders and repair operators as conditional followers, guiding a combined LLM learning and evolutionary search. Experiments on the traveling salesperson problem and capacitated vehicle routing problem demonstrated that SPO surpasses existing strong baselines and generalizes to larger problem instances. AI

IMPACT This framework could lead to more efficient optimization algorithms for complex combinatorial problems.

RANK_REASON The cluster contains an academic paper detailing a new algorithmic framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SPO framework uses LLMs to discover adaptive search operators

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The cluster contains an academic paper detailing a new algorithmic framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xinyi Ke, Kai Li, Junliang Xing, Yifan Zhang, Jian Cheng ·

    SPO: Discovering Adaptive Large Neighborhood Search Operators via Stackelberg Program Optimization

    arXiv:2609.31179v1 Announce Type: new Abstract: Large neighborhood search (LNS) relies critically on destroy and repair operators, whose effectiveness depends on both adaptation to the evolving LNS state and interaction between the two roles. We introduce Stackelberg Program Opti…

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