Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization
Surrogate-assisted evolutionary algorithms (SAEAs) are effective methods for solving expensive optimization problems (EOPs), where surrogate models replace most expensive evaluations and critically influence the final optimization results. In recent years, tabular foundation models have advanced rapidly, and the Tabular Prior-data Fitted Network (TabPFN) has been adopted as a surrogate model for EOPs due to its strong predictive capability, demonstrating promising performance. Motivated by its potential as a surrogate model in SAEAs, this work conducts a comprehensive study that combines extensive experiments with in-depth theoretical analysis to investigate the effectiveness of TabPFN. S...
| Authors | Lu Han, Jin Wang, Yuchen Li et al. |
|---|---|
| Primary category | cs.NE |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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