  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.

Neural and Evolutionary Computing;Artificial Intelligence