Algorithm Evolution Using Large Language Model
Fei Liu Xialiang Tong Mingxuan Yuan Qingfu Zhang
Optimization can be found in many real-life applications. Designing an
effective algorithm for a specific optimization problem typically requires a
tedious amount of effort from human experts with domain knowledge and algorithm
design skills. In this paper, we propose a novel approach called Algorithm
Evolution using Large Language Model (AEL). It utilizes a large language model
(LLM) to automatically generate optimization algorithms via an evolutionary
framework. AEL does algorithm-level evolution without model training. Human
effort and requirements for domain knowledge can be significantly reduced. We
take constructive methods for the salesman traveling problem as a test example,
we show that the constructive algorithm obtained by AEL outperforms simple
hand-crafted and LLM-generated heuristics. Compared with other domain deep
learning model-based algorithms, these methods exhibit excellent scalability
across different problem sizes. AEL is also very different from previous
attempts that utilize LLMs as search operators in algorithms.