Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise. We extend the LLaMEA framework to MOBO, using large language models as mutation and crossover operators within evolutionary strategies to generate complete algorithm implementations, with SMAC hyperparameter optimization integrated into the evolutionary loop.
Across nine evolutionary runs, we generated approximately 900 algorithms and benchmarked them on twelve synthetic problems (ZDT, DTLZ, WFG) and three real-world engineering problems (RE), using a BoFire qParEGO implementation as a state-of-the-art Bayesian-optimization baseline. On the synthetic suite, the strongest generated algorithm attains the highest mean normalized hypervolume (0.971, vs. 0.869 for qParEGO) while requiring roughly 60x less wall-clock time. A Friedman test with post-hoc analysis places the two in a single top-performing group, and per-problem tests find the generated algorithm significantly better than qParEGO on 7 of the 12 problems and never worse, matching state-of-the-art accuracy at an order-of-magnitude lower cost.
On the three unseen real-world engineering problems, a generated algorithm attains the best mean normalized hypervolume (0.985, vs. 0.971 for qParEGO)—significantly better than qParEGO on two of the three problems—at roughly 3.4x lower wall-clock cost, confirming that the gains transfer beyond the synthetic regime. Thus, LLM-driven evolutionary search can discover algorithm designs that achieve Pareto-efficient trade-offs difficult to reach through manual design.
Blogger's Review: This research showcases the potential of large language models in algorithm generation, significantly enhancing the efficiency and effectiveness of Bayesian algorithms through evolutionary strategies. This approach not only reduces reliance on expert knowledge but also broadens the horizons of algorithm design, indicating a promising future for automated algorithm development.