Transferable knowledge graphs with executable learned operators for algorithm design

The paper introduces Generative Executable Algorithm Knowledge Graphs (GEAKG), a graph-based representation that stores procedural knowledge for algorithm design as typed nodes with validated operators, edges encoding admissible compositions, and learned edge weights capturing effective sequences. GEAKG can be instantiated across different domains by altering only a role ontology and binding, enabling transfer of knowledge. Experiments show that within neural‑architecture‑search families, learned snapshots transfer across many dataset pairs, while across combinatorial domains only the ontology‑constrained executable structure transfers, providing significant savings in expensive target‑side searches such as large scheduling instances.

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