LLaMEA: Automatically Generating Metaheuristics with Large Language Models
Bas van Stein, Thomas Bäck · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2025
This paper summarizes our recent work published in IEEE Transactions on Evolutionary Computation (2025), entitled "LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics". In this work we propose a novel evolutionary algorithm framework that leverages large language models (LLMs) like GPT-4 to automatically generate, evaluate, and refine metaheuristic optimization algorithms. Evaluations on the BBOB benchmark suite demonstrate that algorithms automatically discovered by LLaMEA can outperform established state-of-the-art methods, such as CMA-ES and DE, especially on lower-dimensional optimization problems. Our results illustrate the significant potential of using LLMs for automated algorithm design, setting a new direction for evolutionary computation research.