Tag
The paper extends the LLaMEA framework to automatically design multi-objective Bayesian optimization algorithms using large language models as mutation and crossover operators within evolutionary strategies, achieving state-of-the-art accuracy with significantly lower computational cost on synthetic and real-world problems.