arXiv Machine Learning By Xiao Jin, Yongxiong Wang, Haobo Liu, Yudong Du, Yukun Du

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

Read the original on arXiv Machine Learning →

arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.

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arXiv AI
Sep 23

Artificial Neural Networks as Surrogate Models in Black Box Optimization

arXiv:2609.22329v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation...

By Md Khadimul Islam Zim (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic), Martin Hole\v{n}a (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic)
arXiv Machine Learning
Sep 17

Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization

The paper evaluates the Tabular Prior-data Fitted Network (TabPFN) as a surrogate model in surrogate‑assisted evolutionary algorithms (SAEAs) for expensive optimization problems. Through extensive experiments in both offline and online settings across a range of problem types—including single‑objective, multi‑objective, constrained, combinatorial, mixed‑variable, and engineering tasks—the study finds that TabPFN’s effectiveness varies strongly with the problem characteristics. The authors conclude that TabPFN should be used selectively, with customized model management and algorithm design tailored to data availability, landscape complexity, and search‑space properties.

By Lu Han, Jin Wang, Yuchen Li, Haoran Gu, Shulei Liu, Ziyang Shi, Wenao Lu, Handing Wang