Algorithm Evolution Using Large Language Model

Code

• github.com/ai4co/llm-as-hh
  pytorch★ 260
• github.com/ai4co/reevo
  pytorch★ 259
• github.com/FeiLiu36/LLM4MOEA
  none★ 62
• github.com/datphamvn/hsevo
  none★ 33

Abstract

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.

Tasks

Reproductions