It is often very tedious for human experts to design efficient algorithms.
Recently, we have proposed a novel Algorithm Evolution using Large Language
Model (AEL) framework for automatic algorithm design. AEL combines the power of
a large language model and the paradigm of evolutionary computation to design,
combine, and modify algorithms automatically. In this paper, we use AEL to
design the guide algorithm for guided local search (GLS) to solve the
well-known traveling salesman problem (TSP). AEL automatically evolves elite
GLS algorithms in two days, with minimal human effort and no model training.
Experimental results on 1,000 TSP20-TSP100 instances and TSPLib instances show
that AEL-designed GLS outperforms state-of-the-art human-designed GLS with the
same iteration budget. It achieves a 0% gap on TSP20 and TSP50 and a 0.032% gap
on TSP100 in 1,000 iterations. Our findings mark the emergence of a new era in
automatic algorithm design.

我们用大型语言模型结合进化计算范式提出了一种新的算法进化（AEL）框架，用于自动算法设计，并在旅行商问题中使用 AEL 设计了引导算法。实验证明，AEL 设计的引导算法在相同迭代预算下优于人工设计的引导算法，标志着自动算法设计的新纪元的出现。

高效引导局部搜索的设计：进化计算与大型语言模型超越人类的实例

An Example of Evolutionary Computation + Large Language Model Beating  Human: Design of Efficient Guided Local Search

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.

利用大型语言模型进行算法进化，能够自动生成优化算法，减少人工专家和领域知识的需求，在解决推销员旅行问题方面表现优异，具有出色的可伸缩性，与先前的利用语言模型作为搜索操作符的尝试大不相同。