Large Language Models (LLMs) such as GPT-4 have demonstrated their ability to understand natural language and generate complex code snippets. This paper introduces a novel Large Language Model Evolutionary Algorithm (LLaMEA) framework, leveraging GPT models for the automated generation and refinement of algorithms. Given a set of criteria and a task definition (the search space), LLaMEA iteratively generates, mutates and selects algorithms based on performance metrics and feedback from runtime evaluations. This framework offers a unique approach to generating optimized algorithms without requiring extensive prior expertise. We show how this framework can be used to generate novel black-box metaheuristic optimization algorithms automatically. LLaMEA generates multiple algorithms that outperform state-of-the-art optimization algorithms (Covariance Matrix Adaptation Evolution Strategy and Differential Evolution) on the five dimensional black box optimization benchmark (BBOB). The results demonstrate the feasibility of the framework and identify future directions for automated generation and optimization of algorithms via LLMs.

使用 GPT-4 这种大型自然语言模型的新型 LLMEa (Large Language Model Evolutionary Algorithm) 框架能够自动生成和优化算法，我们通过在五维黑盒优化基准 (BBOB) 上的测试结果展示了该框架的可行性，并为进一步的算法自动生成和优化指明了未来的方向。

LLaMEA：自动生成元启发式算法的大型语言模型进化算法