LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms
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 implementa...
| Authors | Georgios Laskaris, Reuben Brasher, Niki van Stein et al. |
|---|---|
| Primary category | cs.NE |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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