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Heuristics are widely used for dealing with complex search and optimizationproblems. However, manual design of heuristics can be often very labourextensive and requires rich working experience and knowledge. This paperproposes Evolution of Heuristic (EoH), a novel evolutionary paradigm thatleverages both Large Language Models (LLMs) and Evolutionary Computation (EC)methods for Automatic Heuristic Design (AHD). EoH represents the ideas ofheuristics in natural language, termed thoughts. They are then translated intoexecutable codes by LLMs. The evolution of both thoughts and codes in anevolutionary search framework makes it very effective and efficient forgenerating high-performance heuristics. Experiments on three widely studiedcombinatorial optimization benchmark problems demonstrate that EoH outperformscommonly used handcrafted heuristics and other recent AHD methods includingFunSearch. Particularly, the heuristic produced by EoH with a low computationalbudget (in terms of the number of queries to LLMs) significantly outperformswidely-used human hand-crafted baseline algorithms for the online bin packingproblem.

Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model