A2DEPT generates complete algorithms for COPs using LLM-driven evolutionary program trees with hybrid selection and repair, reducing mean normalized optimality gap by 9.8% versus strongest AHD baselines on standard benchmarks.
Instead of evolving code alone, it maintains a population of thought-code pairs, where Thoughts describe the strategy in natural language and Codes provide the implementation
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A2DEPT: Large Language Model-Driven Automated Algorithm Design via Evolutionary Program Trees
A2DEPT generates complete algorithms for COPs using LLM-driven evolutionary program trees with hybrid selection and repair, reducing mean normalized optimality gap by 9.8% versus strongest AHD baselines on standard benchmarks.