arXiv AI 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)

Artificial Neural Networks as Surrogate Models in Black Box Optimization

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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
arXiv AI
Jun 30

Bilevel Optimization for Neural Architecture Search

arXiv:2606. 29582v1 Announce Type: cross Abstract: Bilevel optimization has become an influential and widely adopted framework for addressing hierarchical optimization problems in machine learning, providing an effective approach to modeling the interaction between two levels of optimization, with applications such as hyperparameter tuning, meta-learning, adversarial training, and data poisoning.

By Abhishek Shukla, Ankur Sinha, Faiz Hamid
arXiv AI
Jun 9

A Multi-Agent System for IPMSM Design Optimization via an FEA-AI Hybrid Approach

arXiv:2606. 09037v1 Announce Type: new Abstract: Interior permanent magnet synchronous motor (IPMSM) design requires balancing conflicting objectives and multi-physics constraints, while modern optimization workflows face three bottlenecks: manual problem setup, high finite element analysis (FEA) cost, and unreliable surrogate-based search in sparse or out-of-distribution regions.

By Jinseong Han, Sunwoong Yang, Namwoo Kang