A knowledge-first approach to LLM-driven automatic heuristic design in combinatorial optimization yields better discovery efficiency, transfer, and generalization than code-centric baselines by formalizing a distortion-compression trade-off.
Algorithm evolution using large language model
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
LLMs optimize genetic algorithm operations and create a new heuristic AutoPort for port selection and beamforming in fluid antenna systems to maximize min SINR, achieving near-optimal performance in simulations.
MEP uses LLMs in a structured reasoning cycle to evolve improved heuristics for HGS on VRPs, achieving up to 2.7% better solution quality and over 45% reduced runtime.
Fine-tuned LLMs with DAR sampling and DPO outperform off-the-shelf versions on algorithm design tasks and generalize to related settings.
Survey organizing LLM uses for VRP into modeler, designer, and coordinator roles, covering variants, solvers, benchmarks, and two experiments.
Under fixed token budget on Circle Packing, deeper per-candidate reasoning beats generating more shallow candidates, and capable models produce evaluation hacks at higher rates.
citing papers explorer
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Back to the Beginning of Heuristic Design: Bridging Code and Knowledge with LLMs
A knowledge-first approach to LLM-driven automatic heuristic design in combinatorial optimization yields better discovery efficiency, transfer, and generalization than code-centric baselines by formalizing a distortion-compression trade-off.
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LLM-Enabled Automated Algorithm Design for Multiuser Fluid Antenna Communications
LLMs optimize genetic algorithm operations and create a new heuristic AutoPort for port selection and beamforming in fluid antenna systems to maximize min SINR, achieving near-optimal performance in simulations.
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PyVRP$^+$: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems
MEP uses LLMs in a structured reasoning cycle to evolve improved heuristics for HGS on VRPs, achieving up to 2.7% better solution quality and over 45% reduced runtime.
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Fine-tuning Large Language Model for Automated Algorithm Design
Fine-tuned LLMs with DAR sampling and DPO outperform off-the-shelf versions on algorithm design tasks and generalize to related settings.
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Vehicle Routing Problem Meets Large Language Models: An Overview and Perspectives
Survey organizing LLM uses for VRP into modeler, designer, and coordinator roles, covering variants, solvers, benchmarks, and two experiments.
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Effective Harness Engineering for Algorithm Discovery with Coding Agents
Under fixed token budget on Circle Packing, deeper per-candidate reasoning beats generating more shallow candidates, and capable models produce evaluation hacks at higher rates.